This paper proposes a novel model-free adaptive control method with practical prescribed performance for coupled mechatronic systems subject to uncertain friction, unknown delays, mismatched disturbance, and input quantization. Different from the existing ultra-local model-based controllers, a multi-input multi-output (MIMO) ultra-local model with an alpha gain matrix is used to approximate the plant within a short time interval. This allows that the method in this paper can be directly applied to MIMO systems without the decoupling process. A recursive least square technique is utilized to identify the alpha gain matrix. Additionally, a practical prescribed performance function applicable to any initial condition is designed to transform the tracking error, achieving predefined convergence time and pre-assignable tracking precision. Then, a global sliding mode control with adaptive switch gain is constructed to stabilize the transformed error. Afterward, the stability and convergence of the closed-loop system with the designed controller are analyzed by using Lyapunov theorem. The co-simulations on PUMA 560 robotic manipulator and iReHave exoskeleton, and experiment on 2-DOF upper-limb exoskeleton are completed. The obtained results demonstrate the effectiveness and superiority of the proposed control method.

Dual-arm humanoid robots are poised to transform industrial manufacturing automation in human-centric environments. However, unlocking this potential requires a unified framework that can simultaneously handle coupled bimanual coordination, versatile physical interaction, and safety. We introduce Passive Model Predictive Cooperative Interaction Control (P-MPCIC), a framework that co-optimizes task performance and interaction safety under a formal passivity guarantee. P-MPCIC integrates model predictive control for the bimanual subsystem within a whole-body architecture and uses a coupling matrix to enforce synchronization objectives across relative motion and force distribution. For interaction prediction, the framework incorporates a composite robot-environment model that combines parallel and series impedance dynamics, yielding a linear state-space predictor. Passivity is enforced as a constraint on the energy balance at the interaction port, preventing destabilizing energy generation from the controller. We verify the framework’s core principles through planar simulations and demonstrate its practical effectiveness on a 7-DoF dual-arm humanoid.

Recent work has demonstrated the potential of diffusion models in robot bimanual skill learning. However, existing methods ignore the learning of posture-dependent task features, which are crucial for adapting dual-arm configurations to meet specific force and velocity requirements in dexterous bimanual manipulation. To address this limitation, we propose Manipulability-Aware Diffusion Policy (ManiDP), a novel imitation learning method that not only generates plausible bimanual trajectories, but also optimizes dual-arm configurations to better satisfy posture-dependent task requirements. ManiDP achieves this by extracting bimanual manipulability from expert demonstrations and encoding the encapsulated posture features using Riemannian-based probabilistic models. These encoded posture features are then incorporated into a conditional diffusion process to guide the generation of task-compatible bimanual motion sequences. We evaluate ManiDP on six real-world bimanual tasks, where the experimental results demonstrate a 39.33% increase in average manipulation success rate and a 0.45 improvement in task compatibility compared to baseline methods. This work highlights the importance of integrating posture-relevant robotic priors into bimanual skill diffusion to enable human-like adaptability and dexterity.

Dexterous Functional Grasping (DFG) is the crucial first step for humanoid robots to perform generalized manipulation tasks. However, enabling robots to learn language-guided DFG skills in real-world environments presents several challenges, including comprehending the complex relationship between task instructions and grasp functionality, generating feasible functional grasps of dexterous hands, and handling generalization for novel functional concepts. To tackle these challenges, we introduce SayFuncGrasp, a Large Language Model (LLM) based DFG framework that can synthesize versatile dexterous functional grasps from language instructions and achieve generalization on novel functional concepts. SayFuncGrasp first harnesses the open-ended manipulation knowledge from an LLM to infer grasp functionality based on language instructions. Subsequently, it employs the inferred grasp functionality to synthesize plausible DFG actions characterized by hand synergies. Simulation experiments show that SayFuncGrasp significantly outperforms the baseline method in open-set grasp functionality generalization. Real robot experiments demonstrate the effectiveness and generalizability of SayFuncGrasp for interactive humanoid manipulation tasks, achieving an overall grasp success rate of 64.66% and a manipulation success rate of 70.41%. Note to Practitioners—This research was motivated by the practical challenge of enabling humanoid robots with high-DoF dexterous hands to perform functional grasping based on verbal instructions. In industrial settings, such capabilities can significantly enhance the versatility and adaptability of humanoid assistants, allowing them to perform complex manipulations simply by being told what to do, thereby reducing programming complexity and increasing flexibility. Current dexterous functional grasping methods rely solely on visual input, without the ability to process language instructions. Furthermore, they are restricted to pre-defined functional concepts and cannot be generalized to novel object classes and manipulation tasks within natural language. Our newly proposed language-guided dexterous functional grasping system takes advantage of open-ended manipulation knowledge from LLMs to produce generalized functional grasps of dexterous robot hands according to verbal commands. Our experiment results demonstrate improved versatility and generalizability compared to the state-of-the-art.

Industrial exoskeletons are a potential solution for reducing work-related musculoskeletal disorders during carrying or lifting tasks. Having sensors, electrical/pneumatic actuators, and control systems, active exoskeletons present a more versatile control system because it is possible to select different assistive strategies based on the performed task. From this perspective, human-machine interaction is required to safely open basic exoskeleton domains to the user and provide an adaptable setup system. This article presents the assessment and benchmarking of the novel XoLab Natural Language Interface, a voice user interface for interaction and configuration of industrial active exoskeletons. The evaluation of the novel interface was performed by 17 participants who completed the setup and operational activities while wearing the XoTrunk exoskeleton. The benchmark consisted of a comparison of the presented device with previous adaptable interfaces for the exoskeleton: the user command interface and the monitor system interface. The results showed that although the novel interface demonstrated a considerable lag in the time response, it was more attractive, stimulating and novel than the standard one. However, the standard interface obtained favourable results over the user command interface and the voice interface perspicuity and efficiency.

We propose a control pipeline for SAG (Searching, Approaching, and Grasping) of objects, based on a decoupled arm kinematic chain and impedance control, which integrates image-based visual servoing (IBVS). The kinematic decoupling allows for fast end-effector motions and recovery that leads to robust visual servoing. The whole approach and pipeline can be generalized for any mobile platform (wheeled or tracked vehicles), but is most suitable for dynamically moving quadruped manipulators thanks to their reactivity against disturbances. The compliance of the impedance controller makes the robot safer for interactions with humans and the environment. We demonstrate the performance and robustness of the proposed approach with various experiments on our 140 kg HyQReal quadruped robot equipped with a 7-DoF manipulator arm. The experiments consider dynamic locomotion, tracking under external disturbances, and fast motions of the target object.

The assistive profile of an active back support exoskeleton is strongly dependent on the manual tuning of controller gains based on previous experience and trial-and-error. Human-in-the-loop (HIL) optimization allows for automatic tuning of assistive profiles to different subjects. Most HIL methods make use of intrusive sensors that could affect out-of-the-lab exoskeleton adoption. Therefore, we propose a HIL-based assistive controller architecture using only one single IMU that can be easily embedded in any exoskeleton system. To validate our algorithm we recruited 3 subjects and asked them to perform a series of successive load liftings. Meanwhile, we analysed the back-muscles activations focusing on cumulative activation (iEMG), and median activation. We also monitored the total torque generated by the exoskeleton. With respect to an assistance-less condition, the proposed controller resulted in up to 19% reduction of the back-muscles activity. Moreover, compared to a state-of-the-art controller that produced up to 15% reduction of the back-muscles activity, the new controller also required generation of 4% less exoskeleton torque.

Quadrupedal manipulators require to be compliant when dealing with external forces during autonomous manipulation, tele-operation or physical human-robot interaction. This paper presents a whole-body controller that allows for the implementation of a Cartesian impedance control to coordinate tracking performance and desired compliance for the robot base and manipulator arm. The controller is formulated through an optimization problem using Quadratic Programming (QP) to impose a desired behavior for the system while satisfying friction cone constraints, unilateral force constraints, joint and torque limits. The presented strategy decouples the arm and the base of the platform, enforcing the behavior of a linear double-mass spring damper system, and allows to independently tune their inertia, stiffness and damping properties. The control architecture is validated through an extensive simulation study using the 90kg HyQ robot equipped with a 7-DoF manipulator arm. Simulation results show the impedance rendering performance when external forces are applied at the arm's end-effector. The paper presents results for full stance condition (all legs on the ground) and, for the first time, also shows how the impedance rendering is affected by the contact conditions during a dynamic gait.

Shoulder-sideWINDER is an exoskeleton to assist the shoulder movement of workers performing manual tasks. It aims to minimize discomfort caused by a joint misalignment between the shoulder and an exoskeleton, which is a common problem with many closely coupled systems. Based on the shoulder’s musculoskeletal structure, we propose two novel misalignment correction mechanisms: center-of-rotation tracking mechanism and force-guiding mechanism. The center-of-rotation tracking mechanism compensates for the vertical misalignment that arises from the scapulohumeral rhythm. The force-guiding mechanism compensates for the horizontal misalignment due to changes in the plane of the arm elevation. The kinematic performance of reducing joint misalignment was evaluated using the angle ratio as the indicator under two conditions: with and without the center-of-rotation tracking mechanism. The muscle load caused by the kinematic disturbance was evaluated under three conditions: wearing the exoskeleton with and without the center-of-rotation tracking mechanism and not wearing the exoskeleton. The analysis used muscle load data measured by surface electromyography on five shoulder muscles. Five subjects executed 12 arm motions each, in 4 planes of elevation. Since the system was optimized for a pilot user, subjects with similar morphology presented positive results showing that the proposed mechanism reduced the misalignment in the dominant range of working motion. Apart from the anterior deltoid, the loads on muscles responsible for the dominant range of working motion also showed a significant reduction compared to the case without the mechanism. In conclusion, we verified that the Shoulder-sideWINDER provides comfortable support by reducing joint misalignment, and identified directions for design improvements that will further enhance usability.

Humans in contrast to robots are excellent in performing fine manipulation tasks owing to their remarkable dexterity and sensorimotor organization. Enabling robots to acquire such capabilities, necessitates a framework that not only replicates the human behaviour but also integrates the multi-sensory information for autonomous object interaction. To address such limitations, this research proposes to augment the previously developed kernelized synergies framework with visual perception to automatically adapt to the unknown objects. The kernelized synergies, inspired from humans, retain the same reduced subspace for object grasping and manipulation. To detect object in the scene, a simplified perception pipeline is used that leverages the RANSAC algorithm with Euclidean clustering and SVM for object segmentation and recognition respectively. Further, the comparative analysis of kernelized synergies with other state of art approaches is made to confirm their flexibility and effectiveness on the robotic manipulation tasks. The experiments conducted on the robot hand confirm the robustness of modified kernelized synergies framework against the uncertainties related to the perception of environment.

As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills to robots, where human demonstrations represented in Cartesian or joint spaces are utilized to estimate task/skill models that can be subsequently generalized to new situations. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in many others. Despite recent advances in learning orientations from demonstrations, several crucial issues have not been adequately addressed yet. For instance, how can demonstrated orientations be adapted to pass through arbitrary desired points that comprise orientations and angular velocities? In this article, we propose an approach that is capable of learning multiple orientation trajectories and adapting learned orientation skills to new situations (e.g., via-points and end-points), where both orientation and angular velocity are considered. Specifically, we introduce a kernelized treatment to alleviate explicit basis functions when learning orientations, which allows for learning orientation trajectories associated with high-dimensional inputs. In addition, we extend our approach to the learning of quaternions with angular acceleration or jerk constraints, which allows for generating smoother orientation profiles for robots. Several examples including experiments with real 7-DoF robot arms are provided to verify the effectiveness of our method.

In recent years, a myriad of advanced results have been reported in the community of imitation learning, ranging from parametric to non-parametric, probabilistic to non-probabilistic and Bayesian to frequentist approaches. Meanwhile, ample applications (e.g., grasping tasks and humanrobot collaborations) further show the applicability of imitation learning in a wide range of domains. While numerous literature is dedicated to the learning of human skills in unconstrained environments, the problem of learning constrained motor skills, however, has not received equal attention. In fact, constrained skills exist widely in robotic systems. For instance, when a robot is demanded to write letters on a board, its end-effector trajectory must comply with the plane constraint from the board. In this paper, we propose linearly constrained kernelized movement primitives (LC-KMP) to tackle the problem of imitation learning with linear constraints. Specifically, we propose to exploit the probabilistic properties of multiple demonstrations, and subsequently incorporate them into a linearly constrained optimization problem, which finally leads to a non-parametric solution. In addition, a connection between our framework and the classical model predictive control is provided. Several examples including simulated writing and locomotion tasks are presented to show the effectiveness of our framework.

Hybrid legged-wheeled robots such as the CEN-TAURO, are capable of varying their footprint polygon to carry out various agile motions. This property can be advantageous for wheeled-only planning in cluttered spaces, which is our focus. In this paper, we present an improved algorithm that builds upon our previously introduced preliminary footprint varying A* planner, which was based on the rectangular symmetry of the foot support polygon. In particular, we introduce a Theta* based planner with trapezium-like search, which aims to further reduce the limitations imposed upon the wheeled-only navigation of the CENTAURO robot by the low-dimensional search space, maintaining the real-time computational efficiency. The method is tested on the simulated and real full-size CENTAURO robot in cluttered environments.

We present a novel control strategy for dynamic legged locomotion in complex scenarios that considers information about the morphology of the terrain in contexts when only on-board mapping and computation are available. The strategy is built on top of two main elements: first a contact sequence task that provides safe foothold locations based on a convolutional neural network to perform fast and continuous evaluation of the terrain in search of safe foothold locations; then a model predictive controller that considers the foothold locations given by the contact sequence task to optimize target ground reaction forces. We assess the performance of our strategy through simulations of the hydraulically actuated quadruped robot HyQReal traversing rough terrain under realistic on-board sensing and computing conditions.

The ability of legged systems to traverse highly- constrained environments depends by and large on the performance of their motion and balance controllers. This paper presents a controller that excels in a scenario that most state- of-the-art balance controllers have not yet addressed: line walking, or walking on nearly null support regions. Our approach uses a low-dimensional virtual model (2-DoF) to generate balancing actions through a previously derived four- term balance controller and transforms them to the robot through a derived kinematic mapping. The capabilities of this controller are tested in simulation, where we show the 90kg quadruped robot HyQ crossing a bridge of only 6 cm width (compared to its 4 cm diameter spherical foot), by balancing on two feet at any time while moving along a line. Additional simulations are carried to test the performance of the controller and the effect of external disturbances. Lastly, we present our preliminary experimental results showing HyQ balancing on two legs while being disturbed.

A robot in the future may initially has a good learning capability but an empty library of movements. It gradually enriches its library of movements through human demonstrations. Dynamic Movement Primitives (DMPs) has been proved to be an effective way to represent trajectories. Trajectories are classified into discrete and rhythmic ones, and parameters are set for each demonstrated trajectory. However, what kind of trajectory will be provided by robot users is sometimes unknown to robot developers, so trajectory pattern and the parameters can not be determined in advance. It's also impossible for non-technical robot users to set these parameters and determine the pattern of movements they are going to demonstrate. To make it easier for non-expert robot users to programme their robots by demonstration, this work presents an efficient way to deal with these two problems. The effectiveness of the proposed methodology is proved by teaching a robot to clean the whiteboard in different ways and stack a set of cubic boxes in specific order.

Cable-driven joints proved to be an effective solution in a wide variety of applications ranging from medical to industrial fields where light structures, interaction with unstructured and constrained environments and precise motion are required. These requirements are achieved by moving the actuators from joints to the robot chassis. Despite these positive properties a cable-driven robotic arm requires a complex cable routing within the entire structure to transmit motion to all joints. The main effect of this routing is a friction phenomenon which reduces the accuracy of the motion of the robotic device. In this paper a bond-graph approach is presented to model a family of cable-driven joints including a novel friction model that can be easily implemented into a control algorithm to compensate the friction forces induced by the rope sliding into bushings.

Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitude planner ensures the robot stability by imposing limits to the angular acceleration. Our whole-body controller tracks compliantly trunk motions while avoiding slippage, as well as kinematic and torque limits. Despite the use of a simplified model, which is restricted to flat terrain, our approach shows remarkable capability to deal with a wide range of noncoplanar terrains. The results are validated by experimental trials and comparative evaluations in a series of terrains of progressively increasing complexity.

In legged locomotion, the projection of the robot's Center of Mass (CoM) being inside the convex hull of the contact points is a commonly accepted sufficient condition to achieve static balancing. However, some of these configurations cannot be realized because the joint-torques required to sustain them would be above their limits (actuation limits). In this article, we rule out such configurations and define the feasible region, a revisited support region that guarantees both global static stability in the sense of tip-over and slippage avoidance and of existence of a set of joint-torques that are able to sustain the robot's body weight. We show that the feasible region can be employed for the online selection of feasible footholds and CoM trajectories to achieve statically stable locomotion on rough terrains, also in presence of load-intensive tasks. Key results of our approach include the efficiency in the computation of the feasible region using an Iterative Projection (IP) algorithm and the successful execution of hardware experiments on the HyQ robot, that was able to negotiate obstacles of moderate dimensions while carrying an extra 10-kg payload.

In the context of imitation learning, several approaches have been developed so as to transfer human skills to robots, with demonstrations often represented in Cartesian or joint space. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in many others. However, several crucial issues arising from learning orientations have not been adequately addressed yet. For instance, how can demonstrated orientations be adapted to pass through arbitrary desired points that comprise orientations and angular velocities? In this paper, we propose an approach that is capable of learning multiple orientation trajectories and adapting learned orientation skills to new situations (e.g., via-point and end-point), where both orientation and angular velocity are addressed. Specifically, we introduce a kernelized treatment to alleviate explicit basis functions when learning orientations. Several examples including comparison with the state-of-the-art dynamic movement primitives are provided to verify the effectiveness of our method.

Unstructured environments impose several challenges when robots are required to perform different tasks and adapt to unseen situations. In this context, a relevant problem arises: how can robots learn to perform various tasks and adapt to different conditions? A potential solution is to endow robots with learning capabilities. In this line, imitation learning emerges as an intuitive way to teach robots different motor skills. This learning approach typically mimics human demonstrations by extracting invariant motion patterns and subsequently applies these patterns to new situations. In this paper, we propose a novel kernel treatment of imitation learning, which endows the robot with imitative and adaptive capabilities. In particular, due to the kernel treatment, the proposed approach is capable of learning human skills associated with high-dimensional inputs. Furthermore, we study a new concept of correlation-adaptive imitation learning, which allows for the adaptation of correlations exhibited in high-dimensional demonstrated skills. Several toy examples and a collaborative task with a real robot are provided to verify the effectiveness of our approach.

A common strategy to deal with the expensive reinforcement learning (RL) of complex tasks is to decompose them into a collection of subtasks that are usually simpler to learn as well as reusable for new problems. However, when a robot learns the policies for these subtasks, common approaches treat every policy learning process separately. Therefore, all these individual (composable) policies need to be learned before tackling the learning process of the complex task through policies composition. Moreover, such composition of individual policies is usually performed sequentially, which is not suitable for tasks that require to perform the subtasks concurrently. In this paper, we propose to combine a set of composable Gaussian policies corresponding to these subtasks using a set of activation vectors, resulting in a complex Gaussian policy that is a function of the means and covariances matrices of the composable policies. Moreover, we propose an algorithm for learning both compound and composable policies within the same learning process by exploiting the off-policy data generated from the compound policy. The algorithm is built on a maximum entropy RL approach to favor exploration during the learning process. The results of the experiments show that the experience collected with the compound policy permits not only to solve the complex task but also to obtain useful composable policies that successfully perform in their corresponding subtasks.

Hybrid legged-wheeled robots are able to adapt their leg configuration and height to vary their footprint polygons and go over obstacles or traverse narrow spaces. In this paper, we present a variable configuration wheeled motion planner based on the A* algorithm. It takes advantage of the agility of hybrid wheeled-legged robots and plans paths over low-lying obstacles and in narrow spaces. By imposing a symmetry on the robot polygon, the computed plans lie in a low-dimensional search space that provides the robot with configurations to safely negotiate obstacles by expanding or shrinking its footprint polygon. The introduced autonomous planner is demonstrated using simulations and real-world experiments with the CENTAURO robot.

During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty using a single model. This rich set of information is used in combination with the fusion of optimal controllers to learn robot actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains otherwise.

Industry 4.0 demands the heavy usage of robotic mobile manipulators with high autonomy and intelligence. The goal is to accomplish dexterous manipulation tasks without prior knowledge of the object status in unstructured environments. It is important for the mobile manipulator to recognize and detect the objects, determine manipulation pose, and adjust its pose in the workspace fast and accurately. In this research, we developed a stereo vision algorithm for the object pose estimation using point cloud data from multiple stereo vision systems. An improved iterative closest point algorithm method is developed for the pose estimation. With the pose input, algorithms and several criteria are studied for the robot to select and adjust its pose by maximizing its manipulability on a given manipulation task. The performance of each technical module and the complete robotic system is finally shown by the virtual robot in the simulator and real robot in experiments. This study demonstrates a setup of autonomous mobile manipulator for various flexible manufacturing and logistical scenarios.

Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the task-parameterized Gaussian mixture model to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.

In this paper, we present an implementation of a bi-manual teleoperation system, controlled by a human through three-dimensional (3D) skeleton extraction. The input data is given from a cheap RGB-D range sensor, such as the ASUS Xtion PRO. To achieve this, we have implemented a 3D version of the impressive OpenPose package, which was recently developed. The first stage of our method contains the execution of the OpenPose Convolutional Neural Network (CNN), using a sequence of RGB images as input. The extracted human skeleton pose localisation in two-dimensions (2D) is followed by the mapping of the extracted joint location estimations into their 3D pose in the camera frame. The output of this process is then used as input to drive the end-pose of the robotic hands relative to the human hand movements, through a whole-body inverse kinematics process in the Cartesian space. Finally, we implement the method as a ROS wrapper package and we test it on the centaur-like CENTAURO robot. Our demonstrated task is of a box and lever manipulation in real-time, as a result of a human task demonstration.

In this work we introduced a prioritized Cartesian impedance control under the framework of the Quadratic Programming (QP) optimization. In particular, we present a formulation which is simpler than full inverse dynamics, avoids any matrix pseudo-inversion, inverse kinematics computation and considers strict priorities among tasks. Our formulation is based on QP optimization permitting to take into account also explicit inequality constraints. We compare in simulation the tracking results obtained with a classical algebraic implementation against those derived from the proposed QP implementation taking into account joint torque limits. We consider the classical Cartesian impedance controller and a simplified version, also known as Virtual Model Control. Finally the proposed method was implemented and validated on a humanoid upper-body torque controlled robot. Experimental trials involving various physical interaction conditions were executed to demonstrate the performance of the proposed method.

In the context of learning from demonstration, human examples are usually imitated in either Cartesian or joint space. However, this treatment might result in undesired movement trajectories in either space. This is particularly important for motion skills such as striking, which typically imposes motion constraints in both spaces. In order to address this issue, we consider a probabilistic formulation of dynamic movement primitives, and apply it to adapt trajectories in Cartesian and joint spaces simultaneously. The probabilistic treatment allows the robot to capture the variability of multiple demonstrations and facilitates the mixture of trajectory constraints from both spaces. In addition to this proposed hybrid space learning, the robot often needs to consider additional constraints such as motion smoothness and joint limits. On the basis of Jacobian-based inverse kinematics, we propose to exploit robot null-space so as to unify trajectory constraints from Cartesian and joint spaces while satisfying additional constraints. Evaluations of hand-shaking and striking tasks carried out with a humanoid robot demonstrate the applicability of our approach.

Programming by demonstration has recently gained much attention due to its user-friendly and natural way to transfer human skills to robots. In order to facilitate the learning of multiple demonstrations and meanwhile generalize to new situations, a task-parameterized Gaussian mixture model (TP-GMM) has been recently developed. This model has achieved reliable performance in areas such as human-robot collaboration and dual-arm manipulation. However, the crucial task frames and associated parameters in this learning framework are often set by the human teacher, which renders three problems that have not been addressed yet: (i) task frames are treated equally, without considering their individual importance, (ii) task parameters are defined without taking into account additional task constraints, such as robot joint limits and motion smoothness, and (iii) a fixed number of task frames are pre-defined regardless of whether some of them may be redundant or even irrelevant for the task at hand. In this paper, we generalize the task-parameterized learning by addressing the aforementioned problems. Moreover, we provide a novel learning perspective which allows the robot to refine and adapt previously learned skills in a low dimensional space. Several examples are studied in both simulated and real robotic systems, showing the applicability of our approach.

Bipedal robots have gained a lot of locomotion capabilities the past few years, especially in the control level. Navigation over complex and unstructured environments using exteroceptive perception, is still an active research topic. In this paper, we present a footstep planning system to produce foothold placements, using visual perception and proper environment modeling, given a black box walking controller. In particular, we extend a state-of-the-art search-based planning approach (ARA*) that produces 6DoF footstep sequences in 3D space for flat uneven terrain, to also handle rough curved surfaces, e.g. rocks. This is achieved by integrating both a curved patch modeling system for rough local terrain surfaces and a flat foothold contact analysis based on visual range input data, into the existing planning framework. The system is experimentally validated using real-world point clouds, while rough terrain stepping demonstrations are presented on the WALK-MAN humanoid robot, in simulation.

Robotics teleoperation has been extensively studied and considered in the past in several task scenarios where direct human intervention is not possible due to the hazardous environments. In such applications, both communication degradation and reduced perception of the remote environment are practical issues that can challenge the human operator while controlling the robot and attempting to physically interact within the remote workspace. To address this challenge, we introduce a novel shared-autonomy Tele-Interaction control approach that blends the motion commands from the pilot (master side) with locally (slave side) executed autonomous motion and impedance modulators. This enables a remote robot to handle and autonomously avoid physical obstacles during manoeuvring, reduce interaction forces during contacts, and finally accommodate different payload conditions while at the same time operating with a “default” low impedance setting. We implemented and experimentally validated the proposed method both on simulation and on a real robot platform called CENTAURO. A series of tasks, such as maneuvering through the physical constraints of the remote environment in an autonomous manner, pushing and lifting heavy objects with autonomous impedance regulation and colliding with the rigid geometry of the remote environment were executed. The obtained results demonstrate the effectiveness of the shared-autonomy control principles that eventually aim to reduce the level of attention and stress of human pilot while manoeuvring the slave robot, and at the same time to enhance the robustness of the robot during physical interactions even if accidentally occurred.

We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN.

This paper proposes a novel fall control technique based on energy concepts, which can be applied online to mitigate the impact forces incurred during the falling over of humanoids. The technique reduces the total energy using a nonlinear control tool, called energy shaping (ES), and further distributes the reduced energy over multiple contacts by means of energy distribution polygons (EDP). We also include an effective orientation control to safeguard the end-effectors in the event of ground impacts. The performance of the proposed method is numerically evaluated by dynamic simulations under the sudden falling over scenario of the humanoid robot for both lateral and sagittal falls. The effectiveness of the proposed ES and EDP concepts are verified by diverse comparative simulations with total energy, distribution, and impact forces.

As many imitation learning algorithms focus on pure trajectory generation in either Cartesian space or joint space, the problem of considering competing trajectory constraints from both spaces still presents several challenges. In particular, when perturbations are applied to the robot, the underlying controller should take into account the importance of each space for the task execution, and compute the control effort accordingly. However, no such controller formulation exists. In this paper, we provide a minimal intervention control strategy that simultaneously addresses the problems of optimal control and competing constraints between Cartesian and joint spaces. In light of the inconsistency between Cartesian and joint constraints, we exploit the robot null space from an information-theory perspective so as to reduce the corresponding conflict. An optimal solution to the aforementioned controller is derived and furthermore a connection to the classical finite horizon linear quadratic regulator (LQR) is provided. Finally, a writing task in a simulated robot verifies the effectiveness of our approach.

This paper proposes a novel gripper or end-effector named VARO-fi (VARiable Orientable fingers with translation), with the aim of obtaining human like prehensile manoeuvre such as, in-hand manipulation. The 4 fingered VARO-fi consists of 9 degrees of freedom and it can perform several in-hand manipulation tasks which have been described in this paper. Moreover, the gripper is a simplification of previously proposed gripper platform called Dexclar. The derivation of VARO-fi has been presented and its capabilities have been demonstrated by experiments. Although a generic convex payload is considered as a primitive in the design of VARO-fi however, it is capable to address manipulation for other regular shaped payloads, which has been proven by experiments. A comparison is also illustrated in order to underline the strength of the novel gripper with respect to the state of the art.

This paper presents orientation planning algorithms respecting the requirements of task space trajectory generation, particularly in robotics applications. The proposed algorithms fulfill the following conditions: (i) permitting to impose constraints at angular velocity and acceleration in addition to orientation at endpoints; (ii) rendering continuous acceleration profiles even when interpolating multiple orientations; and (iii) being computationally fast enough for realtime implementation. The generated spline trajectories are essentially a concatenation of polynomial in time curves parameterized by quaternion coefficients. To impose the unitariness condition critically required for quaternion representation of orientation, we develop an on-line update mechanism which successively reparameterizes the polynomials constructing the spline, towards suppressing distortions that the normalization operation might incur. Experiments on an anthropomorphic robot upper-body are carried out to demonstrate the efficacy and real-time compatibility of the proposed algorithms in comparison with a standard spherical interpolation method.

Motivated by the desire to have robots physically present in human environments, in recent years we have witnessed an emergence of different approaches for learning active compliance. Some of the most compelling solutions exploit a minimal intervention control principle, correcting deviations from a goal only when necessary, and among those who follow this concept, several probabilistic techniques have stood out from the rest. However, these approaches are prone to requiring several task demonstrations for proper gain estimation and to generating unpredictable robot motions in the face of uncertainty. Here we present a Programming by Demonstration approach for uncertainty-aware impedance regulation, aimed at making the robot compliant - and safe to interact with - when the uncertainty about its predicted actions is high. Moreover, we propose a data-efficient strategy, based on the energy observed during demonstrations, to achieve minimal intervention control, when the uncertainty is low. The approach is validated in an experimental scenario, where a human collaboratively moves an object with a 7-DoF torque-controlled robot.

When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which take into account task space, joint space and force constraints. We treat the problem by considering different torque controllers acting on the robot, whose relevance is learned probabilistically from demonstrations. This information is used to combine the controllers by exploiting the properties of Gaussian distributions, generating new torque commands that satisfy the important features of the task. We validate the approach in two experimental scenarios using 7- DoF torque-controlled manipulators, with tasks that require the consideration of different controllers to be properly executed.

Despite the growing focus on the design of compliant mechanisms for robotics actuators that manifest several advantages in terms of robustness and interaction-related characteristics, the incorporation of elasticity in the actuation drive renders under-damped vibration modes and reduces the bandwidth of the system. The addition of damping principles into compliant systems can address such impediments to accuracy and stability, and enhance the passivity characteristics of the controlled compliant actuator. However, passive damping mechanisms integrated into compliant systems to exhibit user-defined passive dissipation profiles have not been realized. This paper proposes a non-linear stiffness compliant module, and introduces a novel non-linear damper which complements the elastic element. The cam-follower mechanism was employed for rendering the user-defined non-linear behaviour. While the passive compliance of the module is replicated using a curved leaf spring, the passive damping is generated by rolling/sliding motion of a rigid cylinder on an elastomer. The design of the module is described, the theoretical modelling is presented, and experimental results validating the functionality of the proposed design in dissipating under-damped oscillations are demonstrated.

We present a trajectory optimization framework for legged locomotion on rough terrain. We jointly optimize the center of mass motion and the foothold locations, while considering terrain conditions. We use a terrain costmap to quantify the desirability of a foothold location. We increase the gait's adaptability to the terrain by optimizing the step phase duration and modulating the trunk attitude, resulting in motions with guaranteed stability. We show that the combination of parametric models, stochastic-based exploration and receding horizon planning allows us to handle the many local minima associated with different terrain conditions and walking patterns. This combination delivers robust motion plans without the need for warm-starting. Moreover, we use soft-constraints to allow for increased flexibility when searching in the cost landscape of our problem. We showcase the performance of our trajectory optimization framework on multiple terrain conditions and validate our method in realistic simulation scenarios and experimental trials on a hydraulic, torque controlled quadruped robot.

Autonomous legged robots will be required to handle a wide range of tasks in complex environments. While a lot of research has focused on developing their abilities for periodic locomotion tasks, less effort has been invested in devising generalized strategies for dynamic, non-periodic movements. Motion design approaches are frequently enlisted in the form of teleoperation or predefined heuristics in such scenarios. We employ a realistic simulation of the hydraulically actuated HyQ2Max quadrupedal system for investigations on two distinctive tasks: rearing and posture recovery. We present a whole-body optimization methodology for non-periodic tasks on quadrupedal systems. This approach delivers solutions involving multiple contacts without the need for predefined feet placements. The results obtained show the potential of optimization approaches for motion synthesis in the context of complex tasks.

Learning complex manipulation tasks often requires to collect a large training dataset to obtain a model of a specific skill. This process may become laborious when dealing with high-DoF robots, and even more tiresome if the skill needs to be learned by multiple robots. In this paper, we investigate how this learning process can be accelerated by using shared latent variable models for knowledge transfer among similar robots in an imitation setting. For this purpose, we take advantage of a shared Gaussian process latent variable model to learn a common latent representation of robot skills. Such representation is then reused as prior information to train new robots by reducing the learning process to a latent-to-output mapping. We show that our framework exhibits faster training convergence and similar performance when compared to single- and multi-robot models. All experiments were conducted in simulation on three different robotic platforms: WALK-MAN, COMAN and CENTAURO robots.

Robotics in the last decades is moving towards bioinspired solutions in order to develop systems increasingly integrated with the human environment. Among them, legged robots fascinates more and more researchers thanks to their ability of moving in unstructured environment such as the ones typical of earthquakes, where in the near future robots are planned to be send to help humans while performing dangerous tasks. On the base of this findings, the authors propose a novel concept for a jumping humanoid leg, based on the key role played from the structural flexibility. The geometric and dynamic features of this leg have been selected thanks to a targeted set of numerical simulations. An extensive campaign of experimental tests useful for the validation of the numerical model here presented will be a matter of future works.

We propose a reactive locomotion strategy, called height reflex, that is useful to address big elevation changes in the terrain (e.g. when a quadruped robot has to step down from a high platform). In these cases the swing leg can lose mobility creating issues in the subsequent steps. The height reflex is a foot trajectory replanning strategy that redistributes the swing motion (in a smart way) to the stance legs to “lower” the whole trunk and to aid the foothold searching motion. To spread the motion we exploit a massless link model of the robot with virtual dampers at the joints, which is used to replan the feet trajectories. The proposed approach is able to incorporate kinematic limits, it is easy-to-tune, computationally efficient and suitable for real-time implementations. The reflex is implemented and experimentally evaluated on the 80 kg hydraulic quadruped HyQ. With our approach we were able to address high steps, up to 24 cm which is 30% of HyQ leg length and 53% of its retractable leg range.

The paper proposes a method to assess the feasible torque bandwidth for electrically driven torque controllable actuators over its entire torque amplitude range. The method solely relies on the knowledge of hardware parameters and thereby determines the physically feasible torque control bandwidth at a given torque amplitude, independent of a controller. The method yields torque-frequency diagrams that are suitable to benchmark torque controllers, formulate actuator design specifications and compare as well as select actuators for a specific torque control application. The paper exemplifies the method on a WALK-MAN leg actuator with locked actuator output and the more practical case of a varying load inertia.

We present a new method to detect object affordances in real-world scenes using deep Convolutional Neural Networks (CNN), an object detector and dense Conditional Random Fields (CRF). Our system first trains an object detector to generate bounding box candidates from the images. A deep CNN is then used to learn the depth features from these bounding boxes. Finally, these feature maps are post-processed with dense CRF to improve the prediction along class boundaries. The experimental results on our new challenging dataset show that the proposed approach outperforms recent state-of-the-art methods by a substantial margin. Furthermore, from the detected affordances we introduce a grasping method that is robust to noisy data. We demonstrate the effectiveness of our framework on the full-size humanoid robot WALK-MAN using different objects in real-world scenarios.

Developing grasping devices with the capabilities to carry out dexterous tasks similar to human hand are being studied for many decades. To this aim, mathematical analysis such as control of multi-fingered gripper, grasp synthesis algorithms, contact types and their interactions have been explicitly addressed by many researchers. Since human hands are dexterous due to the complex integration of control and numerous sensors, hence they are naturally adaptable to grasp, in-hand manipulation of plurality of object by their construction. On the other hand, artificial grippers require priori knowledge of the payload geometry and configuration to maneuver grasping and manipulation tasks at the very first place. Moreover, theoretical analysis, such as contact kinematics, grasp stability cannot predict the nonholonomic behaviors, and therefore, uncertainties are always present to restrict a maneuver, even though the gripper is kinematically feasible of doing the task. Hence, in general, industrial grippers do exploit simpler mechanisms with least number of fingers and tend to avoid soft materials in the construction primarily to achieve dexterity, reliability, repeatability and speed in the process. However, in-hand manipulation of objects urges certain degrees of flexibility in the gripper design; which is difficult to obtain from a rigid structure and also the use of non-rigid materials reduce speed, accuracy and performance. In this research, a gripper platform named Dexclar (DEXterous reConfigurable moduLAR) is proposed, which addresses the dilemma by combining mechanism and modularity, evaluating payload centric requirements.

Posture body variation is one of the ways in which humans skillfully and naturally augment their motion and strength capabilities along specific task-space directions in order to successfully perform complex manipulation tasks. Posture variation also has a significant role in robot manipulation, where manipulability arises as a useful criterion to analyze and control the robot dexterity as a function of its joint configuration. In this context, this paper introduces the promising idea of manipulability transfer, a method that allows robots to learn and reproduce desired manipulability ellipsoids from expert demonstrations. The proposed framework is built on a tensor-based formulation of Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. This geometry-aware method is used to design a manipulability-based redundancy resolution that allows the robot to modify its posture so that its manipulability ellipsoid coincides with the desired one. Experiments in simulation validate the functionality of the proposed approach, which extends the robot learning capability beyond trajectory, force and impedance learning approaches.

In this paper, the well-known problem of codependence between inverse dynamics torque and contact force in bimanual object manipulation is addressed. The common contact constraint, namely rigid grasping, is exploited to decompose the set of dynamics equations into two orthogonally decoupled sets. Subsequently, the inverse dynamics control is formulated in a sub-manifold that is independent of the contact force, leading to analytically correct solutions that do not need to resort to common approximations for the aforementioned codependence problem. The contact force is also analytically computed and, therefore, can be optimally distributed using the torque redundancy. Relying on this prediction is most significant in situations where a force sensor at the end-effector is not present or is faulty. Even in the availability of sensory data, the predicted force may be used to correct typically noisy or delayed when filtered measurements, resulting in improved robustness. Simulation experiments on a planar bimanual manipulation model are presented.

The identification of inertial parameters is crucial to achieve high-performance model-based control of legged robots. The inertial parameters of the legs are typically not altered during expeditions and therefore are best identified offline. On the other hand, the trunk parameters depend on the modules mounted on the robot, like a motor to provide the hydraulic power, or different sets of cameras for perception. This motivates the use of recursive approaches to identify online mass and the position of the Center of Mass (CoM) of the robot trunk, when a payload change occurs. We propose two such approaches and analyze their robustness in simulation. Furthermore, experimental trials on our 80-kg quadruped robot HyQ show the applicability of our strategies during locomotion to cope with large payload changes that would otherwise severely compromise the balance of the robot.

In the context of robotic control, synergies can form elementary units of behavior. By specifying task-dependent coordination behaviors at a low control level, one can achieve task-specific disturbance rejection. In this work we present an approach to learn the parameters of such low-level controllers by demonstration. We identify a synergy by extracting covariance information from demonstration data. The extracted synergy is used to derive a time-invariant state feedback controller through optimal control. To cope with the non-Euclidean nature of robot poses, we utilize Riemannian geometry, where both estimation of the covariance and the associated controller take into account the geometry of the pose manifold. We demonstrate the efficacy of the approach experimentally in a bimanual manipulation task.

Incremental progress in humanoid robot locomotion over the years has achieved important capabilities such as navigation over flat or uneven terrain, stepping over small obstacles and climbing stairs. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-body contact motion planning guided through physical human demonstrations. Our goal is to make the humanoid locomotion problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the whole-body motion and multi-body contacts including both the upper and lower body limbs. The proposed motion planning algorithm is applied to a challenging task of climbing over a large obstacle. We demonstrate successful execution of the climbing task in simulation using our multi-contact motion planning algorithm initialized via a transfer from real-world human demonstrations of the task and further optimized.

This paper proposes a set of five ethical principles, together with seven high-level messages, as a basis for responsible robotics. The Principles of Robotics were drafted in 2010 and published online in 2011. Since then the principles have influenced, and continue to influence, a number of initiatives in robot ethics but have not, to date, been formally published. This paper remedies that omission.

While only continuous motions are possible, the way in which contacts appear and disappear confers to legged locomotion a characteristic discontinuous nature that is traditionally shared by the algorithms used for legged locomotion planning. In this paper, we show that this discontinuous nature can disappear if the notion of collision is well redefined and we efficiently solve two different practical problems of legged locomotion planning with algorithms based on an approach that establishes a bridge between discrete and continuous planning. The first problem consists of reactive footstep planning with a biped robot and the second one consists of nongaited locomotion planning with a hexapod.

Despite the important role of simulation in the development and control of robotics systems, the majority of open source simulation tools has however paid no attention to the progress and paradigm change on the robot design in the past 15 years with the consideration of soft actuation technologies as a mean to power new robotic systems. More specifically, the integration of series elastic actuators (SEAs) into robots modifies significantly the dynamics characteristics of the system while the incorporation of the passive compliance into the actuators is not applied in conventional simulators. This paper introduces a scheme for the implementation of the SEA dynamics on a Gazebo-ROS framework exploited for the simulation of a new centaur-like robot. This approach is based on designing a custom control plugin embodying the passive compliance dynamics so that the controller associated with each joint receives both collocated and non-collocated feedback. A simulation comparison with Matlab validating the performance of the designed control plugin is demonstrated. In the end, a whole-body simulation of the centaur robot driven/controlled by the proposed plugin is presented.

As a major inspiration of biologically inspired systems, multi-legged robots have been developed due to their superior stability feature resulting from their large support polygon. The leg design of a majority of such robots is motivated by the skeleton of vertebrates such as dogs, or that of invertebrates such as spiders. Despite a wide variety of multi-pedal robots on the basis of the two aforesaid leg designs, a thorough comparison of the two underlying design principles remains to be done. This work addresses this problem and presents a comparative study for the two mammal-like and spider-like designs by looking at the joint torque profile, the responsive motion of the legs, and the thrust force applied by the robot. To this end, a set of performance indexes are defined based on the gravity compensation torque, the dynamic manipulability polytope and the force polytope, and evaluated in various leg configurations of the two designs.

In this work a novel synergy-based bilateral tele-manipulation strategy is introduced. The proposed algorithm has been primarily developed to remotely control the Pisa/IIT SoftHand (SH) using a 3-finger hand exoskeleton as master device. With a single actuator and a sensory system limited to a position encoder and a current sensor, the SH minimalist design promotes robustness but challenges traditional teleoperation strategies. To tackle this challenge, the concept of Cartesian-based hand synergies is introduced as a projection tool which maps the fingertip Cartesian space to the directions oriented along the grasp principal components. The unconstrained motion of the operator's hand is projected on this space to extract the SH's motor position reference. Conversely, the interaction force estimated at the robotic hand as a 1-dimensional force along the first synergy is projected to the 9D fingertip Cartesian space through an inverse projection. The resultant finger-individualized forces form a synergy based weighted representation of the grasping effort applied by the SH and are displayed to the operators fingertips using the force feedback hand exoskeleton. The system's ability to reflect the environment's impedance has been experimentally validated during a ball squeezing experiment. To assess the overall effectiveness of the proposed system as a manipulation interface, the SoftHand was mounted on the humanoid robot COMAN and the setup was subsequently enriched with a vision-based tracking system monitoring the operators wrist trajectory. Experimental results indicate that the proposed body-machine bilateral interface allows for the intuitive performance of stable grasps and transport of a large range of diversely shaped objects.

The general approach to humanoid feet design considers the use of rectangular plate sole structures that are relatively stiff and compatible for flat terrain locomotion. Although this can be adequate the inability of these stiff feet to cope with small terrain irregularities, it makes them inappropriate for rough terrains. Toward improvement of humanoids feet this paper presents mechanism of new variable compliant humanoid feet which can provide functionality and adaptability to humanoids locomotion on uneven terrains. The proposed feet design introduces new toe mechanism in the feet with variable stiffness implemented using a leaf spring and rubber balls in series. We present the mechanism design and the implementation of the sensor, and discuss the estimation of variable stiffness range and coefficient of damping at sole. A prototype of the feet was built and experimental results are included to validate the feet design.

This paper focuses on the development of a dynamic model-free whole-body controller for a humanoid robot with high kinematic redundancy. The proposed controller is based on force-level operational-space control framework, which computes joint torques for the required forces of prioritized multiple tasks. While typical approaches based on this framework require to obtain an accurate robot dynamics model, which has been generally recognized as a major hurdle to overcome for implementation in real humanoid robots, the proposed controller incorporates adaptive sliding-mode and online dynamics estimation schemes; thus, it can be easily realized on a humanoid without identifying complex robot dynamic parameters. As a result, the gains of the proposed controller are adaptively adjusted to assure the control accuracy, when the humanoid robot changes its posture and undergoes uncertain disturbances. Experiments with a 23-DoFs humanoid under uncertain disturbances verify that the proposed controller can robustly perform multiple tasks with high accuracy.

Most animal and human locomotion behaviors for solving complex tasks involve dynamic motions and rich contact interaction. In fact, complex maneuvers need to consider dynamic movement and contact events at the same time. We present a hierarchical trajectory optimization approach for planning dynamic movements with unscheduled contact sequences. We compute whole-body motions that achieve goals that cannot be reached in a kinematic fashion. First, we find a feasible CoM motion according to the centroidal dynamics of the robot. Then, we refine the solution by applying the robot's full-dynamics model, where the feasible CoM trajectory is used as a warm-start point. To accomplish the unscheduled contact behavior, we use complementarity constraints to describe the contact model, i.e. environment geometry and non-sliding active contacts. Both optimization phases are posed as Mathematical Program with Complementarity Constraints (MPCC). Experimental trials demonstrate the performance of our planning approach in a set of challenging tasks.

The deployment of robots to assist in environments hostile for humans during emergency scenarios require robots to demonstrate enhanced physical performance, that includes adequate power, adaptability and robustness to physical interactions and efficient operation. This work presents the design and development of the lower body of the new high performance humanoid WALK-MAN, a robot developed recently to assist in disaster response scenarios. The paper introduces the details of the WALK-MAN lower-body, highlighting the innovative design optimization features considered to maximize the leg performance. Starting from the general lower body specifications the objectives of the design and how they were addressed are introduced, including the selection of the leg kinematics, the arrangement of the actuators and their integration with the leg structure to maximize the range of motion, reduce the leg mass and inertia, and shape the leg mass distribution for better dynamic performance. Physical robustness is ensured with the integration of elastic transmission and impact energy absorbing covers. Experimental walking trials demonstrate the correct operation of the legs while executing a walking gait.

A common disadvantage of multi-legged robots is that they often lack the manipulation capability. To overcome this limitation, an arm can be added to the body of the multi-legged robot, to perform manipulation tasks and provide assistance for locomotion. First, we proposed an attachment configuration of the arm for a multi-legged robot that provide a uniform workspace in front, below and above the base robot trunk. Second, an integrated control framework promises to keep the mobility and the balance of the mobile platform and provides precise manipulation capability of the arm incorporating a payload estimation scheme. Finally, we verify an integrated control framework with experimental results of a static and walking mobile platform while moving the arm.

Programming by Demonstration (PbD) offers a user-friendly way to transfer skills from human to robot. Typically, demonstration data do not contain the control inputs required to reproduce the demonstrated skill. These can be obtained from a low-level controller that tracks the modeled movement. We present a PbD approach for minimal intervention control - a control strategy that only corrects perturbations that interfere with task performance. The novelty of our approach is the probabilistic encoding of the movement duration, providing a performance measure that enables minimal intervention control in a temporal sense. This is achieved by combining a probabilistic movement encoding based on Hidden Semi-Markov Model (HSMM) with Model Predictive Control (MPC). The probabilistic model is used to construct an objective function, hereby assuming that variance is a measure for task performance. The proposed method is demonstrated in a robot experiment and compared with our earlier work.

This paper describes a new task-oriented grasping method to reorient a rigid object to its nominal pose, which is defined as the configuration that it needs to be grasped from, in order to successfully execute a particular manipulation task. Our method combines two key insights: (1) a visual 6 Degree-of-Freedom (DoF) pose estimation technique based on 2D-3D point correspondences is used to estimate the object pose in real-time and (2) the rigid transformation from the current to the nominal pose is computed online and the object is reoriented over a sequence of steps. The outcome of this work is a novel method that can be effectively used in the preparatory phase of a manipulation task, to permit a robot to start from arbitrary object placements and configure the manipulated objects to the nominal pose, as required for the execution of a subsequent task. We experimentally demonstrate the effectiveness of our approach on a full-size humanoid robot (WALK-MAN) using different objects with various pose settings under real-time constraints.

We present a novel and real-time method to detect object affordances from RGB-D images. Our method trains a deep Convolutional Neural Network (CNN) to learn deep features from the input data in an end-to-end manner. The CNN has an encoder-decoder architecture in order to obtain smooth label predictions. The input data are represented as multiple modalities to let the network learn the features more effectively. Our method sets a new benchmark on detecting object affordances, improving the accuracy by 20% in comparison with the state-of-the-art methods that use hand-designed geometric features. Furthermore, we apply our detection method on a full-size humanoid robot (WALK-MAN) to demonstrate that the robot is able to perform grasps after efficiently detecting the object affordances.

This contribution compares two approaches for applying disturbance observers (DOBs) to the torque control problem of series elastic actuators (SEAs). It is demonstrated that they are in fact equivalent for linear models in terms of their ability to reject disturbances and enforce nominal model dynamics. The closed loop and error transfer functions for the DOB-based approaches are compared to a fully linear plant and a nonlinear plant without DOB. Simulations demonstrate that the DOBs are able to increase the bandwidth of the nonlinear plant significantly, up to that of the linear plant. Furthermore, the DOBs significantly increase the tracking accuracy at low frequencies.

Exoskeletons offer an intuitive method for actuating multiple DOF of the body; this makes them attractive for applications where generation and coupling of artificial forces to the limbs is needed. Force feedback hand exoskeletons have been continuously considered for whole hand haptic interaction in virtual reality simulators, in teleoperation setups and for rehabilitation. In hand exoskeletons finger tracking, actuation and transmission systems must be embedded in confined spaces, matching at the same time the profound dexterity of the hand transparently and without causing a burden. Most of the design approaches for such systems have remained largely experimental due to hardware limitations, impacting heavily on important functional and ergonomic factors. This paper presents the design of a novel 3-digit hand exoskeleton, which addresses the issues of finger tracking and force feedback. It proposes a new approach for the application of the feedback force with a single attachment at the fingertip through a 6DoF kinematic chain. This kinematic linkage allows for unconstrained reach of the fingers within their full workspace and facilitates a sensor system for high resolution 6DOF tracking of the fingertips. At the same time the highly under-actuated mechanism permits application of a bidirectional feedback force at the fingertips. The hand exoskeleton fits an large range of hand sizes and requires no mechanical alignment between the linkage and the fingers, whatsoever. Preliminary results show the efficacy of this system as a tracking and force feedback device for the hand.

In this paper we present an interactive system to enhance bilateral teleoperation through online virtual fixtures generation and task switching. This is achieved using a stereo camera system which provides accurate information of the surrounding environment of the robot and of the tasks that have to be performed in it. The use of the proposed approach aims at improving the performances of bilateral teleoperation systems by reducing the human operator workload and increasing both the implementation and the execution efficiency. In fact, using our method virtual guidances do not need to be programmed a priori but they can be instead automatically generated and updated making the system suitable for unstructured environments. We strengthen the proposed method using passivity control in order to safely switch between different tasks while teleoperating under active constraints. A series of experiments emulating real industrial scenarios are used to show that the switch between multiple tasks can be passively and safely achieved and handled by the system.

The work presented herein, attempts to address the problem of designing stepping recovery controllers for compliantly actuated humanoid robots. Based on the decomposition of the stepping procedure into three distinct phases, which are characterized by unique combinations of configurations and impedance levels, the contrivance of a Linear Quadratic Regulator (LQR) optimization process allows for the production of a corresponding number of controllers. The penalties associated with the proposed cost functions, which account for compliant dynamics and balance-related parameters alike, are selected in a systematic manner that facilitates the generation of the appropriate impedance levels required for each particular phase of the stepping motion. Subsequently, the superimposition of gravity compensation control, onto the original LQR controllers, renders them nonlinear and theoretically capable of tracking referential stepping trajectories. The associated referential motor positions are then generated by exploiting a formula relating the Centre-of-Pressure (CoP) to the compliant ankle dynamics, thereby satisfying the balancing constraints whilst also accounting for the system's inherent under-actuation. Thus, the technique's novelty stems from its explicit consideration of flexible joint dynamics and deflection torques, for the design of the desired impedance levels and joint stepping trajectories. The Series Elastic Actuator (SEA) powered COmpliant huMANoid (COMAN), has served as an avatar of the stepping recovery methodology that is expounded in the paper.

Robots are becoming safe and smart enough to work alongside people not only on manufacturing production lines, but also in spaces such as houses, museums, or hospitals. This can be significantly exploited in situations in which a human needs the help of another person to perform a task, because a robot may take the role of the helper. In this sense, a human and the robotic assistant may cooperatively carry out a variety of tasks, therefore requiring the robot to communicate with the person, understand his/her needs, and behave accordingly. To achieve this, we propose a framework for a user to teach a robot collaborative skills from demonstrations. We mainly focus on tasks involving physical contact with the user, in which not only position, but also force sensing and compliance become highly relevant. Specifically, we present an approach that combines probabilistic learning, dynamical systems, and stiffness estimation to encode the robot behavior along the task. Our method allows a robot to learn not only trajectory following skills, but also impedance behaviors. To show the functionality and flexibility of our approach, two different testbeds are used: a transportation task and a collaborative table assembly.

The computation of skin forces and deformations for tactile rendering requires an accurate model of the extremely nonlinear behavior of the skin. In this work, we investigate the characterization of finger mechanics with the goal of designing accurate nonlinear models for tactile rendering. First, we describe a measurement setup that enables the acquisition of contact force and contact area in the context of controlled finger indentation experiments. Second, we describe an optimization procedure that estimates the parameters of strain-limiting deformation models that match best the acquired data. We show that the acquisition setup allows the measurement of force and area information with high repeatability, and the estimation method reaches nonlinear models that match the measured data with high accuracy.

In this paper, a robot learning approach is pro- posed which integrates Visuospatial Skill Learning, Imitation Learning, and conventional planning methods. In our approach, the sensorimotor skills (i.e., actions) are learned through a learning from demonstration strategy. The sequence of per- formed actions is learned through demonstrations using Visu- ospatial Skill Learning. A standard action-level planner is used to represent a symbolic description of the skill, which allows the system to represent the skill in a discrete, symbolic form. The Visuospatial Skill Learning module identifies the underlying constraints of the task and extracts symbolic predicates (i.e., action preconditions and effects), thereby updating the planner representation while the skills are being learned. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be planned and performed inde- pendently during the learning phase. Preliminary experimental results on the iCub robot are presented.

It is well known that kinematics can significantly affect the manipulation capabilities of robotic arms, traditionally illustrated by performance indices such as workspace volume, kinematic and force manipulability, and isotropy within the arm workspace. In the case of dual-arm systems and bimanual manipulation tasks, the kinematics effects to the above indices becomes even more apparent. However, in spite of the large number of dual-arm systems developed in the past, there is a little literature on the kinematic design analysis for the development of such systems. Particularly, the effects of configuration/ orientation of the shoulders' placement with respect to the torso structure have not sufficiently studied or considered, while many dual-arm systems with upward and/or forward tilt angle in shoulder base frame have been introduced. This paper addresses this problem and quantifies the effect of shoulders base frame orientation in a dual-arm manipulation system by looking at its effect on several important manipulation indices, such as the overall and common workspace, redundancy, global isotropy, dual-arm manipulability, and inertia ellipsoid index within the common workspace of the two arms. Consequently, a range of upward and forward tilt angles for the shoulder frames is identified for the design of a dual-arm torso system to render the most desired manipulation performance.

In laser-based laryngeal surgeries, motorized laser scanners offer greater aiming accuracy and efficiency. In this paper, a new motorized laser micromanipulator is presented, which is based on a spherical orienting device. It is a 2 degrees-of-freedom roll/pitch mechanism which actuates the laser beamsplitter mirror for improved aiming control and automated intraoperative planning. The combination of this device with state-of-the-art reflective laser focusing optics overcomes the drawbacks of an earlier prototype, providing increased operating distance and surgical range. This makes the device more suitable to real surgical scenarios in the operating room (OR). Improved system accuracy and usability is successfully demonstrated through comparative user trials against the traditional manual laser micromanipulator. The new device offers greater than 57% improvement in accuracy demonstrating its safety and usability. Preliminary ex-vivo trials were also performed with expert surgeons with the new mechanism. The surgeons evaluated the system positively and provided valuable and favourable feedback pointing to the suitability of the device for the OR and its potential to enhance the capacity of laser-based transoral microsurgeries.

In this paper we present a feed forward controller to regulate the depth of laser incisions in soft tissue. Such a controller is compatible with the requirements of laser microsurgery, where space constraints limit the use of sensing devices. The controller is based on an inverse model that maps the desired incision depth to the required laser exposure time. This model is extracted from experimental data through the use of statistical learning methods. To prove the concept, the controller is implemented in a robot-assisted laser microsurgery system that enables precision control of exposure time and laser motion. The validity and the accuracy of the controller is verified experimentally on ex-vivo muscle tissue (chicken breast), revealing an RMSE of 0.12 mm for incisions ranging up to 1 mm. In addition, we demonstrate how the model can be used to implement the automatic ablation of entire volumes of tissue, through the superposition of controlled laser incisions.

Autonomous manipulation of objects requires reliable information on robot-object contact state. Underwater environments can adversely affect sensing modalities such as vision, making them unreliable. In this paper we investigate underwater robot-object contact perception between an autonomous underwater vehicle and a T-bar valve using a force/torque sensor and the robot's proprioceptive information. We present an approach in which machine learning is used to learn a classifier for different contact states, namely, a contact aligned with the central axis of the valve, an edge contact and no contact. To distinguish between different contact states, the robot performs an exploratory behavior that produces distinct patterns in the force/torque sensor. The sensor output forms a multidimensional time-series. A probabilistic clustering algorithm is used to analyze the time-series. The algorithm dissects the multidimensional time-series into clusters, producing a one-dimensional sequence of symbols. The symbols are used to train a hidden Markov model, which is subsequently used to predict novel contact conditions. We show that the learned classifier can successfully distinguish the three contact states with an accuracy of 72% ± 12 %.

The development of variable impedance actuators (VIAs) has highlighted the need for proper control of passive impedance to attain suitable interaction performance. Until recently the regulation of the intrinsic impedance in VIAs is achieved in an open-loop model-based manner, mainly due to the lack of physical sensors capable of measuring impedance components such as stiffness and damping. Hence, the estimation of variable stiffness and damping has been explored, with the target to provide monitoring and feedback for potential closed loop control schemes. However, the use of the output of these estimators in the feedback control of variable impedance actuators has never been implemented/demonstrated in practice. This work contributes to the field with the development and experimental evaluation of a novel damping feedback control for a class of variable impedance compliant actuators able to realize a variable physical damping principle. The scheme is based on non-model-based damping estimation feedback to compensate model uncertainties in the action of an inner controller that uses a model-based friction estimator. Experimental results demonstrate the ability of the proposed scheme to replicate with good fidelity constant and time-varying damping levels.

Encoders have been an inseparable part of robots since the very beginning of modern robotics in the 1950s. As a result, the foundations of robot control are built on the concepts of kinematics and dynamics of articulated rigid bodies, which rely on explicitly measuring the robot configuration in terms of joint angles - done by encoders. In this paper, we propose a radically new concept for controlling robots called Encoderless Robot Control (EnRoCo). The concept is based on our hypothesis that it is possible to control a robot without explicitly measuring its joint angles, by measuring instead the effects of the actuation on its end-effector. To prove the feasibility of this unconventional control approach, we propose a proof-of-concept control algorithm for encoderless position control of a robot's end-effector in task space. We demonstrate a prototype implementation of this controller in a dynamics simulation of a two-link robot manipulator. The prototype controller is able to successfully control the robot's end-effector to reach a reference position, as well as to track continuously a desired trajectory. Notably, we demonstrate how this novel controller can cope with something that traditional control approaches fail to do: adapt on-the-fly to changes in the kinematics of the robot, such as changing the lengths of the links.

In this paper, we propose an Energy based Fall Prediction (EFP) which observes the real-time balance status of a humanoid robot during standing. The EFP provides an analytic and quantitative measure of the level of balance. Both simulation and experimental studies were conducted and compared with the previously proposed indicators, such as Capture Point (CP) and Foot Rotation Indicator (FRI). The EFP also suggests the balance augmentation by active foot tilting to create larger potential barriers. As a proof of concept, a hybrid balance controller was designed to stabilize the robot including under-actuation phases so the robot can also balance with shoes. Our study reveals that both EFP and CP successfully predict falling about 0.2s in advance for the tested robot, while the FRI fails due to the light weight of the foot and limited resolution of the force/torque measurement.

A fundamental aspect of controlling humanoid robots lies in the capability to exploit the whole body to perform tasks. This work introduces a novel whole body control library called OpenSoT. OpenSoT is combined with joint impedance control to create a framework that can effectively generate complex whole body motion behaviors for humanoids according to the needs of the interaction level of the tasks. OpenSoT gives an easy way to implement tasks, constraints, bounds and solvers by providing common interfaces. We present the mathematical foundation of the library and validate it on the compliant humanoid robot COMAN to execute multiple motion tasks under a number of constraints. The framework is able to solve hierarchies of tasks of arbitrary complexity in a robust and reliable way.

We present a framework for dynamic quadrupedal locomotion over challenging terrain, where the choice of appropriate footholds is crucial for the success of the behaviour. We build a model of the environment on-line and on-board using an efficient occupancy grid representation. We use Any-time-Repairing A* (ARA*) to search over a tree of possible actions, choose a rough body path and select the locally-best footholds accordingly. We run a n-step lookahead optimization of the body trajectory using a dynamic stability metric, the Zero Moment Point (ZMP), that generates natural dynamic whole-body motions. A combination of floating-base inverse dynamics and virtual model control accurately executes the desired motions on an actively compliant system. Experimental trials show that this framework allows us to traverse terrains at nearly 6 times the speed of our previous work, evaluated over the same set of trials.

Agile robot locomotion on rough terrain is highly dependent on the ability to perceive the environment. In this paper, we show how the interaction between a reactive control framework and an online mapping system can significantly improve the trotting performance on irregular terrain. In particular, this new locomotion controller increases the stability of the robot and reduces frontal leg and shin collisions with obstacles by correcting in realtime the foothold locations. The mapping system uses an RGB-D sensor and a motion capture system to build a three dimensional map of the surroundings of the robot. While the robot is trotting, the control framework requests in advance a local heightmap around the next nominal foothold position. Then, an optimized foot placement location is estimated by applying visual pattern classification on the acquired heightmaps, and the leg endpoint trajectory is modified accordingly. The foothold correction is performed independently for each leg. To show the effectiveness of our approach the controller was tested both in simulation and experimentally with our 80 kg hydraulic quadruped robot, HyQ. The results show that visual based reaction through pattern classification is a promising approach to increase locomotion robustness over challenging terrain.

The work presents the development and evaluation of a novel foot sole for humanoid robots. For humanoid locomotion the foot sole is important for absorbing impacts. In contrast to the simple planar rubber pad foot sole that is conventionally used in humanoid robots this paper introduces a new foot sole design in which the dissipation of energy during collision is done effectively using a viscous air damping sole mechanism that provides better reduction of the ground impact forces. The paper describes the principle of the foot sole and provides details of its design and implementation. Experimental trials were performed with the child size humanoid robot, COMAN, wearing the proposed feet to validate their performance during landing and walking. The results demonstrate that the proposed new passive damping mechanism can reduce effectively the ground reaction impact forces and oscillations while maintaining the foot/body posture.

This paper challenges the well-established assumption in robotics that in order to control a robot it is necessary to know its kinematic information, that is, the arrangement of links and joints, the link dimensions and the joint positions. We propose a kinematic-free robot control concept that does not require any prior kinematic knowledge. The concept is based on our hypothesis that it is possible to control a robot without explicitly measuring its joint angles, by measuring instead the effects of the actuation on its end-effector. We implement a proof-of-concept encoderless robot controller and apply it for the position control of a physical 2-DOF planar robot arm. The prototype controller is able to successfully control the robot to reach a reference position, as well as to track a continuous reference trajectory. Notably, we demonstrate how this novel controller can cope with something that traditional control approaches fail to do: adapt to drastic kinematic changes such as 100% elongation of a link, 35-degree angular offset of a joint, and even a complete overhaul of the kinematics involving the addition of new joints and links.

We propose a new algorithm capable of online regeneration of gait patterns. The algorithm uses a nonlinear optimization technique to find step parameters that will bring the robot from the present state to a desired state. It modifies online not only the footstep positions, but also the step timing in order to maintain dynamic stability during walking. Inclusion of step time modification extends the robustness against rarely addressed disturbances, such as pushes towards the stance foot. The controller is able to recover dynamic stability regardless of the source of the disturbance (e.g. model inaccuracy, reference tracking error or external disturbance). We describe the robot state estimation and center-of-mass feedback controller necessary to realize stable locomotion on our humanoid platform COMAN. We also present a set of experiments performed on the platform that show the performance of the feedback controller and of the gait pattern regenerator. We show how the robot is able to cope with series of pushes, by adjusting step times and positions.

We propose a novel control framework to demonstrate a unique foot tilting maneuver based on ankle torque control for humanoid balance recovery. The framework consists of the variable impedance regulation at the center of mass of the robot based on the ankle torque control, the virtual stoppers to prevent over tilting of the feet, and the body attitude control. The scope of our paper focuses on the sagittal scenario as the first proof of concept on the balance recovery by means of active foot tilting without losing stability. Our study demonstrates the success of the control implementation for the humanoid push recovery and the feasibility of having actively controlled foot tilting. The experimental data are presented and analyzed.

Human-robot collaboration seeks to have humans and robots closely interacting in everyday situations. For some tasks, physical contact between the user and the robot may occur, originating significant challenges at safety, cognition, perception and control levels, among others. This paper focuses on robot motion adaptation to parameters of a collaborative task, extraction of the desired robot behavior, and variable impedance control for human-safe interaction. We propose to teach a robot cooperative behaviors from demonstrations, which are probabilistically encoded by a task-parametrized formulation of a Gaussian mixture model. Such encoding is later used for specifying both the desired state of the robot, and an optimal feedback control law that exploits the variability in position, velocity and force spaces observed during the demonstrations. The whole framework allows the robot to modify its movements as a function of parameters of the task, while showing different impedance behaviors. Tests were successfully carried out in a scenario where a 7 DOF backdrivable manipulator learns to cooperate with a human to transport an object.

Very often, when addressing the problem of human-robot skill transfer in task space, only the Cartesian position of the end-effector is encoded by the learning algorithms, instead of the full pose. However, orientation is just as important as position, if not more, when it comes to successfully performing a manipulation task. In this paper, we present a framework that allows robots to learn the full poses of their end-effectors in a task-parameterized manner. Our approach permits the encoding of complex skills, such as those found in bimanual manipulation scenarios, where the generalized coordination patterns between end-effectors (i.e. position and orientation patterns) need to be considered. The proposed framework combines a dynamical systems formulation of the demonstrated trajectories, both in ℝ3and SO(3), and task-parameterized probabilistic models that build local task representations in both spaces, based on which it is possible to extract the relevant features of the demonstrated skill. We validate our approach with an experiment in which two 7-DoF WAM robots learn to perform a bimanual sweeping task.

This paper aims at developing a unified and adaptive foot placement control for legged robots. The locomotion control of legged robots can be classified into three parts as body height control, body attitude control, and forward velocity control. In our study, the body attitude is controlled at stance phase by the hip actuator, and the height is controlled by the motion of the stance leg. In this case, the foot placement has a nearly linear correlation with forward velocity. Hereby, a generic foot placement controller is developed to control the forward velocity based on the online linear regression analysis of their coupled correlation. Our proposed algorithm is capable of adjusting the control parameters automatically, and is featured by good adaptability and higher control accuracy that outperforms the empirical tuning. The very same controller is able to produce stable hopping with accurate forward velocity tracking even with unknown mass offset, as well as stable bipedal running and walking with accurate velocity tracking.

In this paper, we resolve the issue of stepping over a large obstacle by exploiting the redundancy of pelvis rotation and the versatility of foot trajectories for the humanoids. The control framework consists of a motion pattern that exploits the redundancy of pelvis rotation to enlarge the kinematic workspace, a generic foot trajectory generation which can be modified by a parametric interface to adapt to a specific task as well as utilizing the hip abduction to avoid obstacle collision. Moreover, the compensation strategies are also presented for reducing the discrepancies to implement the dynamic stepping motion on a real robot. The effectiveness is validated by COMAN's capability of dynamically stepping over a large obstacle of 10cm height by 5cm width which is almost 20% of its leg length in both simulation and experiment.

Brain-computer interfaces (BCIs) offer high potential for enhancing training in many tasks, especially those that require maintaining high levels of concentration such as surgery. Training focus and attention can play a critical role in surgery since concentration on the task at hand is fundamental to prevent life-threatening errors. In this paper we propose a new method for concentration training in the context of robot-assisted laser microsurgery associated to a feedback design that makes the interaction more intuitive. This approach couples augmented reality (AR) features to both BCI-based on-line measurement of the user's mental focus and the control of the surgical robot. The methodology is described as a brain-controlled augmented reality (BcAR) training system. AR is used to maintain the surgeon's perceptual contact with the real operating setting, while focus stimulation is provided by modifying features of an AR item based on real-time monitoring of the user's mental state. In this research a low-cost EEG device is used and the BcAR is implemented in the form of an AR scalpel that behaves as a "retractable" knife according to the user's mental focus: low concentration levels retract the knife and prevent cutting. This design provides directional compatibility between the AR feedback animation and the spontaneous motion of user's attention along the AR tool, resulting in an intuitive system with real impact on the training outcome. This is demonstrated through user trials and comparison with training based on simple AR feedback (no EEG). Results demonstrate the potential of the approach, showing a significant improvement in post-training task execution time without any detriment to user experience. Subjective questionnaires also confirmed the critical role of directional compatibility in the AR feedback. Such findings allow the identification of further improvements and novel potential applications of this interaction paradigm.

Increasingly, robots are designed to interact with the environment, including humans and tools. Legged robots, in particular, have to deal with environmental contacts every time they take a step. To handle these interactions properly, it is desirable to be able to set the robot's dynamic behavior, i.e., its impedance. In this contribution, we investigate the most relevant theoretical and practical aspects in impedance control using hydraulic actuators, ranging from the force dynamics analysis and model-based controller design to the overall stability and performance assessment. We present results with one leg of the quadruped robot HyQ and also highlight the influence of hardware parameters, such as valve bandwidth and inertia, in the impedance and force tracking. In addition, we demonstrate the capabilities of HyQ's actively compliant leg by experimentally comparing it with a passively compliant version of the same leg. With such a broad spectrum of analyses and discussions, this paper aims to serve as a practical and comprehensive guide for implementing high-performance impedance control on highly dynamic hydraulic robots.

This paper investigates learning approaches for discovering fault-tolerant control policies to overcome thruster failures in Autonomous Underwater Vehicles (AUV). The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the vehicle. When a fault is detected and isolated the model of the AUV is reconfigured according to the new condition. To discover a set of optimal solutions a multi-objective reinforcement learning approach is employed which can deal with multiple conflicting objectives. Each optimal solution can be used to generate a trajectory that is able to navigate the AUV towards a specified target while satisfying multiple objectives. The discovered policies are executed on the robot in a closed-loop using AUV's state feedback. Unlike most existing methods which disregard the faulty thruster, our approach can also deal with partially broken thrusters to increase the persistent autonomy of the AUV. In addition, the proposed approach is applicable when the AUV either becomes under-actuated or remains redundant in the presence of a fault. We validate the proposed approach on the model of the Girona500 AUV.

Several studies showed the potential of interaction technologies for assisting the performance of users during motor tasks. In particular, repetitive exercises can take advantage of novel interaction strategies in order to sustain the users efforts to maintain the target level of performance. This paper introduces a paradigm for performing a drag and drop movement of Augmented Reality 3D objects from marker to marker, using a smartphone. This interaction paradigm permits to perform repetitive exercises which can be implemented in upper limb motor training and rehabilitation tasks. After introducing the interaction paradigm, the paper describes the methodology and the results of a pilot study about the effects of AR dynamic features on the users movements and experience during repetitive motor tasks.

The use of typical position controllers for robots working around humans can involve some risks when unintended physical human-robot interactions occur. In order to benefit from a proper tracking performance during normal operations, and a smooth and damped recovery from position errors due to contacts with external objects/agents, Proxy-based Sliding Mode Control was proposed. While the efficacy of this controller in fully actuated manipulators was discussed, the employment of this controller in underactuated systems has not been studied so far. This paper introduces a control scheme to implement this controller in a class of underactuated systems. Specifically, the control of flexible joint manipulators possessing passive elastic elements in series with motors is studied. The formulation of Proxy-based Sliding Mode Control is adopted according to the stability requirements of this type of dynamic systems, and a torque controller required for the regulation of the the output torque of actuation units is designed using the Feedback Linearization and the Linear Quadratic optimal control approach. The performance of the proposed scheme is demonstrated in dynamic simulation of an anthropomorphic compliant arm.

We investigate methods to improve fault-tolerance of Autonomous Underwater Vehicles (AUVs) to increase their reliability and persistent autonomy. We propose a learning-based approach that is able to discover new control policies to overcome thruster failures as they happen. The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the AUV. The model is adapted to a new condition when a fault is detected and isolated. Since the approach generates an optimal trajectory, the learned fault-tolerant policy is able to navigate the AUV towards a specified target with minimum cost. Finally, the learned policy is executed on the real robot in a closed-loop using the state feedback of the AUV. Unlike most existing methods which rely on the redundancy of thrusters, our approach is also applicable when the AUV becomes under-actuated in the presence of a fault. To validate the feasibility and efficiency of the presented approach, we evaluate it with three learning algorithms and three policy representations with increasing complexity. The proposed method is tested on a real AUV, Girona500.

Robot learning from demonstrations requires the robot to learn and adapt movements to new situations, often characterized by position and orientation of objects or landmarks in the robot's environment. In the task-parameterized Gaussian mixture model framework, the movements are considered to be modulated with respect to a set of candidate frames of reference (coordinate systems) attached to a set of objects in the robot workspace. Following a similar approach, this paper addresses the problem of having missing candidate frames during the demonstrations and reproductions, which can happen in various situations such as visual occlusion, sensor unavailability, or tasks with a variable number of descriptive features. We study this problem with a dust sweeping task in which the robot requires to consider a variable amount of dust areas to clean for each reproduction trial.

A new challenge for surgical robotics is placed in the use of flexible manipulators, to perform procedures that are impossible for currently available rigid robots. Since the surgeon only controls the end-effector of the manipulator, new control strategies need to be developed to correctly move its flexible body without damaging the surrounding environment. This paper shows how a positional controller for a new surgical robot (STIFF-FLOP) can be learnt from the demonstrations given by an expert user. The proposed algorithm exploits the variability of the task to comply with the constraints only when needed, by implementing a minimal intervention principle control strategy. The results are applied to scenarios involving movements inside a constrained environment and disturbance rejection.

We present a task-parameterized probabilistic model encoding movements in the form of virtual spring-damper systems acting in multiple frames of reference. Each candidate coordinate system observes a set of demonstrations from its own perspective, by extracting an attractor path whose variations depend on the relevance of the frame at each step of the task. This information is exploited to generate new attractor paths in new situations (new position and orientation of the frames), with the predicted covariances used to estimate the varying stiffness and damping of the spring-damper systems, resulting in a minimal intervention control strategy. The approach is tested with a 7-DOFs Barrett WAM manipulator whose movement and impedance behavior need to be modulated in regard to the position and orientation of two external objects varying during demonstration and reproduction.

In electronic manufacturing system, the design of the robotic hand with sufficient dexterity and configuration is important for the successful accomplishment of the assembly task. Due to the growing demand from high-mix manufacturing industry, it is difficult for the traditional robot to grasp a large number of assembly parts or tools having cylinder shapes with correct postures. In this research, a novel jaw like gripper with human-sized anthropomorphic features is designed for in-hand precise positioning and twisting online. It retains the simplicity feature of traditional industrial grippers and dexterity features of dexterous grippers. It can apply a constant gripping force on assembly parts and performs reliable twisting movement within limited time to meet the industrial requirements. Manipulating several cylindrical assembly parts by robot, as an experimental case in this paper, is studied to evaluate its performance. The effectiveness of proposed gripper design and mechanical analysis is proved by the simulation and experimental results.

This paper focuses on how to generate dynamic transitions in order to make our robot COMAN (COmpliant huMANoid) dynamically traverse inclined terrains. The novel approach addresses dynamic walking on inclined surfaces by dividing the walking motion into two phases: transition and incline walking. During the transition phase, the humanoid robot performs a 3-dimensional movement in order to transfer its body between surfaces of different inclinations, which is then followed by the incline-walking phase. The transition phase is less trivial to execute than the incline walking itself. In this paper, we first formulate the equations of a 3D (non linear) Inverted Pendulum, and then we derive an equivalent model. Subsequently, we introduce a trajectory generator based on this model and validate it experimentally by performing, with COMAN, dynamic transitions from the horizontal ground to a 10° slope.

Ergonomic and human-centered approaches are increasingly important in the design of surgeon-machine interfaces. In the case of microsurgeries, the procedures suffer from susceptibility to variation in surgeon skill and equipment characteristics. This paper presents a novel, computer-assisted surgical interface for laser-based microsurgeries, called the “μRALP Surgical System”. With the system, surgeries can be performed with improved safety and precision using a three-part architecture: (i) a 3D viewer device providing stereoscopic visualization; (ii) a graphics stylus that controls a motorized micromanipulator for laser aiming and activation; and (iii) a configuration interface allowing system setup and modifications in real-time. The system combines the advantages of a computer-assisted platform while respecting the visualization and manipulation requirements of a microsurgical procedure. The features include intraoperative planning for automatic laser incisions and ablations as well as safety regions based on virtual overlays in the surgeon's field-of-view. A comparative evaluation of the proposed system against the traditional system points to the clear superiority of the new interface. The quantitative comparison shows that the proposed interface is safer, more precise, and better controlled. The qualitative comparison demonstrates that the interface is easier to use, easier to learn, and has a minimal training requirement. The technological advances presented here shall lead to enhanced interfaces, increasing the capacity of surgical systems through user-centered design approaches.

This paper investigates application of machine learning to the problem of contact perception between a robot's gripper and an object. The input data comprises a multidimensional time-series produced by a force/torque sensor at the robot's wrist, the robot's proprioceptive information, namely, the position of the end-effector, as well as the robot's control command. These data are used to train a hidden Markov model (HMM) classifier. The output of the classifier is a prediction of the contact state, which includes no contact, a contact aligned with the central axis of the valve, and an edge contact. To distinguish between contact states, the robot performs exploratory behaviors that produce distinct patterns in the time-series data. The patterns are discovered by first analyzing the data using a probabilistic clustering algorithm that transforms the multidimensional data into a one-dimensional sequence of symbols. The symbols produced by the clustering algorithm are used to train the HMM classifier. We examined two exploratory behaviors: a rotation around the x-axis, and a rotation around the y-axis of the gripper. We show that using these two exploratory behaviors we can successfully predict a contact state with an accuracy of 88 ± 5 % and 81 ± 10 %, respectively.

Recently-developed variable damping mechanisms have been exploited as a complement to compliant actuators. While accurate knowledge and control of generated damping is essential for achieving the desired performance, no physical sensor measuring the damping exists. This work introduces a novel non-model-based approach for the estimation of time-variant damping for variable impedance actuation systems. The approach is based only on torque and position/velocity measurements; without the knowledge of system's inputs, to ensure the estimation of both intentional and unintentional changes. Hence, a recursive least square estimator, modified for achieving a proper convergence for the estimation of time-variant parameters, is exploited. Experiments on a variable physical damping actuator are also presented to validate the performance of proposed approach.

This work focuses on the modeling and control of robotic manipulators powered by compliant actuation systems equipped with clutches for providing friction torque on demand. A novel control scheme is proposed for modulating the clutch friction torque in this particular class of compliant actuators to make the robot operate in “Rigid mode” when it does not interact with the environment to achieve high accuracy, bandwidth and controllability; meanwhile ensuring that the robot maximum static force is constrained to a maximum threshold permitting flexible reactions in potentially risky scenarios. The robot autonomously switches to “Compliant mode” (clutches off) when it interacts with external agents to exploit the advantages of compliance during contacts. Experimental results are presented to show the effectiveness of proposed approach in improving the robot performance (tracking accuracy) while still guaranteeing an interaction-friendly behavior when contact occurs.

On a new demand of safe human-robot interaction for robotic applications, the Compact Compliant Actuator, named CompActTM, is recently developed with physical compliance and active variable damping. In this mechanism, a desired physical damping behavior is realized by generating a friction force which is actively controlled by piezoelectric actuators (PEAs). However, nonlinearities such as hysteresis and creep effect make difficult to precisely control the generated piezoelectric force. This paper focuses on a development of precise force tracking controller for PEAs. A time delay estimation (TDE) using a force feedback is newly proposed to compensate a hysteretic behavior of the PEA and external uncertainties without a mathematical model. Thanks to the force-based TDE, the proposed control is accurate, computationally efficient and easily implementable on the real PEA system. The proposed control scheme is experimentally verified on the CompActTM. Root-mean-square values of the steady-state error for step commands are kept as less than error ratio of 0.13 % and the closed-loop system bandwidth for sinusoidal commands of 20 N stroke is confirmed as about 11 Hz under 100 N payload. In addition, the stability of the proposed control is proved to be bounded-input-bounded-output (BIBO) stable.

The balance control of humanoid robots against external perturbations is a fundamental prerequisite for operating in unstructured environments where physical interaction may unexpectedly occur. These balancing actions can be very demanding in terms of power and torque requirements for ankle joints especially after strong and sudden impacts. In this work, an optimal control problem is formulated for the linearized inverted pendulum model to reduce the peak power requirements during ankle balancing strategy. This optimal control which reduces peak torque and power is computed numerically and approximated by a piecewise linear function of the states called the approximate optimal compliance regulator. The balancing ability of this compliance regulator is evaluated against other optimal compliance methods. The stability of the linearly switching approximated optimal compliance regulator is determined from practical perspective using quadratic stability and parameter dependent Lyapunov functions. The efficacy of the proposed stabilizer is validated for a compliant humanoid.

This paper presents a novel hybrid actuator with controllable mechanical damping. It has been developed to provide subsequently the actuation means for haptic interfaces that can demonstrate intrinsic passive performance when rendering hard contacts. The overall actuator is a dual actuation system where one actuator is responsible for generating the joint motion while the second is dedicated to regulating the physical damping through a semi-active friction mechanism. This semi active friction mechanism applies a purely dissipative torque on the joint, which can be continuously controlled to render damping levels ranging from completely free to heavily damped and even a completely locked joint. The present work focuses mainly on the mechatronic details of the actuator design and in particular on the modelling and control of the damper. The proposed variable damping mechanism is evaluated in a simple 1-DOF joint. Experimental results are presented to demonstrate that the unit is capable of replicating physical damping with adequate performance.

We present a framework for quadrupedal locomotion over highly challenging terrain where the choice of appropriate footholds is crucial for the success of the behaviour. We use a path planning approach which shares many similarities with the results of the DARPA Learning Locomotion challenge and extend it to allow more flexibility and increased robustness. During execution we incorporate an on-line force-based foothold adaptation mechanism that updates the planned motion according to the perceived state of the environment. This way we exploit the active compliance of our system to smoothly interact with the environment, even when this is inaccurately perceived or dynamically changing, and update the planned path on-the-fly. In tandem we use a virtual model controller that provides the feed-forward torques that allow increased accuracy together with highly compliant behaviour on an otherwise naturally very stiff robotic system. We leverage the full set of benefits that a high performance torque controlled quadruped robot can provide and demonstrate the flexibility and robustness of our approach on a set of experimental trials of increasing difficulty.

This paper presents a passivity based compliance stabilizer for humanoid robots. The proposed stabilizer is an admittance controller that uses the force/torque sensing in feet to actively regulate the compliance for the position controlled system. The low stiffness provided by the stabilizer permits compliant interaction with external forces, and the active damping control guarantees the passivity by dissipating the excessive energy delivered by disturbances. Both the theoretical work and simulation validations are presented. The effectiveness of the stabilizer is demonstrated by the simulations of a simplified cart-table model and the multi-body model of a humanoid under impulsive/periodic force perturbations during standing and walking in place. Simulation data show the quantitative evaluation of the stabilization effect by comparing the responses of body attitude, center of mass, center of pressure without and with the stabilizer.

In electronic manufacturing system, the design of the robotic hand with sufficient dexterity and configuration is important for the successful accomplishment of the assembly task. It is significant that the robot can grasp assembly parts and do some simple in-hand manipulation so as to fit them with the package slots. In this research, we study the process of precise in-hand posture transition problem using a novel jaw like gripper with human-sized anthropomorphic features. We transform the in-hand manipulation problem into a series of static grasping problems. Then we study the successful twisting condition on each grasp frame by analyzing its dynamic performance and requirements. Based on this data-driven idea, simulation and experimental data is obtained from both successful and failed trials. Finally, we create the distribution of parameters grasp map for successful twisting.

This work presents an approach for exploring unknown surfaces with discontinuities using only force/torque information. The motivation is to build an information map of an unknown object or environment by performing a fully-autonomous haptic exploration. Examples of discontinuities considered here are contours with sharp turns (such as wall corners) and abrupt dips (such as cliffs). Compliant motion control using force information has the ability to conform to unknown, smooth surfaces but not to discontinuous surfaces. This paper investigates solutions to address the limitation in compliant motion control over discontinuities while maintaining a desired normal force along the surface. We propose two methods to address the problem: (1) superposition of motion and force control and (2) rotation of axes for force and motion control. The theoretical principles are discussed and experimental results with a KUKA lightweight arm moving in 2D space are presented. Both approaches successfully negotiate objects with sharp 90-degree and 120-degree turns while still maintaining good tracking of the desired force.

The need for safe human-robot interaction in emerging robotic applications has recently driven the development of new range of actuation systems spanning from variable stiffness drives to variable damping or full variable impedance joints. Concerning the provision of variable physical damping in compliant actuators, the main objective is to improve the control of compliant joint. In a particular class of these variable physical damping actuators (VPDAs), the level of generated damping is realized by creating a friction force which is actively controlled by piezoelectric actuators (PEAs). Therefore, to effectively control the damping output, the accurate force control of the PEAs is required. However, difficulties to precisely control the generated piezoelectric force stem from its highly nonlinear behavior such as hysteresis and creep effect. This paper presents a novel practical force tracking controller for PEAs with unknown hysteresis behavior. The proposed control consists of two elements: terminal sliding-mode based desired dynamics injection and model-free compensation for nonlinear dynamics of PEAs, which allow fast convergence and extraordinary robustness to the closed-loop system. The stability of the overall system is proved in the sense of Lyapunov. On the real prototype of VPDA, the proposed control scheme is experimentally verified and analyzed by comparison to other controllers, demonstrating improved force tracking performance of PEAs in VPDA system.

This work introduces a novel balance monitoring strategy for humanoid robots. The proposed method addresses the problem of ensuring the balance maintenance of a humanoid robot, through the online monitoring of its state of balance by means of a Lyapunov (energy) function. The proposed method involves the use of dynamical models accounting for both the link and motor states. Energy limits corresponding to the front and rear edges of the support polygon are computed using a closed-loop Lyapunov function. Therefore, this method focuses on the resolution of two issues through a single control scheme, namely, guaranteeing asymptotical stability of the robot at the joint level, in addition to ensuring that it maintains its dynamical balance. A mathematical proof of the previous claims, as well as of the method's validity, is provided in the paper, whereby a direct relationship between the CoP and the system's energy has been established for the first time. Experimental results of step recovery and walking tests performed on the COmpliant huMANoid (COMAN) corroborate the method's applicability and performance as a balance monitor.

Physical interaction between humans and robots arises a large set of challenging problems involving hardware, safety, control and cognitive aspects, among others. In this context, the cooperative (two or more people/robots) transportation of bulky loads in manufacturing plants is a practical example where these challenges are evident. In this paper, we address the problem of teaching a robot collaborative behaviors from human demonstrations. Specifically, we present an approach that combines: probabilistic learning and dynamical systems, to encode the robot's motion along the task. Our method allows us to learn not only a desired path to take the object through, but also, the force the robot needs to apply to the load during the interaction. Moreover, the robot is able to learn and reproduce the task with varying initial and final locations of the object. The proposed approach can be used in scenarios where not only the path to be followed by the transported object matters, but also the force applied to it. Tests were successfully carried out in a scenario where a 7 DOFs backdrivable manipulator learns to cooperate, with a human, to transport an object while satisfying the position and force constraints of the task.

This paper proposes an automated robotic system to perform cell microinjections to relieve human operators from this highly difficult and tedious manual procedure. The system, which uses commercial equipment currently found on most biomanipulation laboratories, consists of a multitask software framework combining computer vision and robotic control elements. The vision part features an injection pipette tracker and an automatic cell targeting system that is responsible for defining injection points within the contours of adherent cells in culture. The main challenge is the use of bright-field microscopy only, without the need for chemical markers normally employed to highlight the cells. Here, cells are identified and segmented using a threshold-based image processing technique working on defocused images. Fast and precise microinjection pipette positioning over the automatically defined targets is performed by a two-stage robotic system which achieves an average injection rate of 7.6 cells/min with a pipette positioning precision of 0.23 μm. The consistency of these microinjections and the performance of the visual targeting framework were experimentally evaluated using two cell lines (CHO-K1 and HEK) and over 500 cells. In these trials, the cells were automatically targeted and injected with a fluorescent marker, resulting in a correct cell detection rate of 87% and a successful marker delivery rate of 67.5%. These results demonstrate that the new system is capable of better performances than expert operators, highlighting its benefits and potential for large-scale application.

Skills can often be performed in many different ways. In order to provide robots with human-like adaptation capabilities, it is of great interest to learn several ways of achieving the same skills in parallel, since eventual changes in the environment or in the robot can make some solutions unfeasible. In this case, the knowledge of multiple solutions can avoid relearning the task. This problem is addressed in this paper within the framework of Reinforcement Learning, as the automatic determination of multiple optimal parameterized policies. For this purpose, a model handling a variable number of policies is built using a Bayesian non-parametric approach. The algorithm is first compared to single policy algorithms on known benchmarks. It is then applied to a typical robotic problem presenting multiple solutions.

Research in learning from demonstration has focused on transferring movements from humans to robots. However, a need is arising for robots that do not just replicate the task on their own, but that also interact with humans in a safe and natural way to accomplish tasks cooperatively. Robots with variable impedance capabilities opens the door to new challenging applications, where the learning algorithms must be extended by encapsulating force and vision information. In this paper we propose a framework to transfer impedance-based behaviors to a torque-controlled robot by kinesthetic teaching. The proposed model encodes the examples as a task-parameterized statistical dynamical system, where the robot impedance is shaped by estimating virtual stiffness matrices from the set of demonstrations. A collaborative assembly task is used as testbed. The results show that the model can be used to modify the robot impedance along task execution to facilitate the collaboration, by triggering stiff and compliant behaviors in an on-line manner to adapt to the user's actions.

Innovative design features of user interfaces could assist a patient during the accomplishment of repetitive physical exercises in training and rehabilitation. Dual task paradigms can induce a change in attentional focus allocation, moving focus away from a body part which is affected by fatigue. This study proposes an interface which integrates the motion tracking capabilities of a Kinect with the touch screen input data and vibration feedback of a smart phone. This interface is exploited to design and evaluate a dual task paradigm tested through an experimental study. Purpose of such paradigm is to support upper arm rehabilitation, by generating a distraction from the target area of the limb during repetitive tasks. The data collected during the experiments and the interface itself will be analyzed and discussed.

Autonomous valve turning is an extremely challenging task for an Autonomous Underwater Vehicle (AUV). To resolve this challenge, this paper proposes a set of different computational techniques integrated in a three-layer hierarchical scheme. Each layer realizes specific subtasks to improve the persistent autonomy of the system. In the first layer, the robot acquires the motor skills of approaching and grasping the valve by kinesthetic teaching. A Reactive Fuzzy Decision Maker (RFDM) is devised in the second layer which reacts to the relative movement between the valve and the AUV, and alters the robot's movement accordingly. Apprenticeship learning method, implemented in the third layer, performs tuning of the RFDM based on expert knowledge. Although the long-term goal is to perform the valve turning task on a real AUV, as a first step the proposed approach is tested in a laboratory environment.

We propose a reactive controller framework for robust quadrupedal locomotion, designed to cope with terrain irregularities, trajectory tracking errors and poor state estimation. The framework comprises two main modules: One related to the generation of elliptic trajectories for the feet and the other for control of the stability of the whole robot. We propose a task space CPG-based trajectory generation that can be modulated according to terrain irregularities and the posture of the robot trunk. To improve the robot's stability, we implemented a null space based attitude control for the trunk and a push recovery algorithm based on the concept of capture points. Simulations and experimental results on the hydraulically actuated quadruped robot HyQ will be presented to demonstrate the effectiveness of our framework.

The CompAct™ actuator features a clutch mechanism placed in parallel with its passive series elastic transmission element and can therefore benefit from the advantages of both series elastic actuators (SEA) and rigid actuators. The actuator is capable of effectively managing the storage and release of the potential energy of the compliant element by the appropriate control of the clutch subsystem. Controlling the timing of the energy storage/release in the elastic element is exploited for improving motion control in this research. This paper analyses how this class of actuation systems can be used to maximize the link velocity of the joint. The dynamic model of the joint is derived and an optimal control strategy is proposed to identify optimal input reference profiles for the actuator (motor position/velocity and clutch activation timing) which permit the link velocity maximization. The effect of compliance of the joint on the performance of the system is studied and the optimal stiffness is analyzed.

State-of-the-art laser phonomicrosurgery (LP) used for the treatment of laryngeal abnormalities involves complex otolaryngological surgical techniques. It relies heavily on surgeon dexterity, requiring significant psychomotor skills. Equipment scale and size, laser operative distance, and the anatomically small nature of the vocal folds all combine to compound the surgical challenges. An objective measurement is therefore necessary to understand the impact of equipment design, its usability, surgeon skill, and learning, on performing LP effectively. This paper introduces imaging based feature extraction as a method to establish metrics to assess surgical performance in LP. Experimental analysis demonstrates the utility of these metrics in measuring surgical task execution vis-à-vis the task objectives. The metrics also provide for a combined rating scale giving a robust quantitative classification of the levels of surgical performance.

In this paper, a knee exoskeleton device and its Tele-Impedance based assistive control scheme is presented. The exoskeleton device is an inherently compliant actuated system that was implemented based on the series elastic actuation (SEA) to provide improved and intrinsically soft interaction behaviour. Details of the exoskeleton design are presented. A detailed musculoskeletal model was developed and experimentally identified in order to map electromyographic signals to the antagonistic muscle torques, acting on the human knee joint. The estimated muscle torques are used in order to determine the user's intent and joint stiffness trend. These reference signals are exploited by a novel Tele-Impedance controller which is applied to a knee exoskeleton device to provide assistance and stiffness augmentation to the user's knee joint. Experimental trials of a standing-up motion task were carried out for evaluation of the proposed control strategy. The results indicate that the proposed knee exoskeleton device and control scheme can effectively generate assistive actions that are intrinsically and naturally controlled by the user muscle activity.

Octopus arms, as well as elephant trunks, squid tentacles, and vertebrate tongues are termed muscular-hydrostats. In such structures, the volume of the organ remains constant during their motions, enabling diverse, complex, and highly controlled movements without the support of a skeleton. Such flexible structures show major advantages over articulated arms that have a rigid skeleton and joints. These advantages have been attracting roboticists aiming to apply these material properties to soft robot controls. In this paper, we show that the muscular-hydrostat system itself has the computational capacity to achieve a complex nonlinear computation. By using a 3D dynamic simulator of the system inspired by the octopus, we actually demonstrate that the system is capable of emulating complex nonlinear dynamical systems by exploiting its elastic body dynamics as a computational resource. In addition, we systematically analyze its computational power in terms of memory capacity, and show that the system has an intrinsic and characteristic short term memory profile. Finally, the implications for soft robot control and future application scenarios are discussed.

This paper presents a sufficient condition to establish the existence of unique equilibrium points for three types of gravity compensation controller when applied to over-actuated systems. As compared to the existing work, this paper extends the current theory by introducing feedback gain matrices that are not constrained to being diagonal and positive definite. The inherently COmpliant huMANoid (COMAN) served as a platform for the validation of the designed gravity compensation controller that employed reference link positions. The displayed experimental results provide evidence of successful link tracking of sinusoidal references on a humanoid system composed of series elastic actuators.

The incorporation of passive compliance in robotic systems could improve their performance during interactions and impacts, for energy storage and efficiency, and for general safety for both the robots and humans. This paper presents the recently developed COMpliant huMANoid COMAN. COMAN is actuated by passive compliance actuators based on the series elastic actuation principle (SEA). The design and implementation of the overall body of the robot is discussed including the realization of the different body segments and the tuning of the joint distributed passive elasticity. This joint stiffness tuning is a critical parameter in the performance of compliant systems. A novel systematic method to optimally tune the joint elasticity of multi-dof SEA robots based on resonance analysis and energy storage maximization criteria forms one of the key contributions of this work. The paper will show this method being applied to the selection of the passive elasticity of COMAN legs. The first completed robot prototype is presented accompanied by experimental walking trials to demonstrate its operation.

This paper presents an Octopus inspired walking robot with pneumatic muscle actuator (PMA) driven continuum arms. Each arm is made up of 4 longitudinally arranged PMAs, consistent with octopus arm anatomy. We first present the design and construction of a single continuum arm followed by its modeling and experimental validation. The design of the walking robot is then presented followed by details of extended dynamic model describing the full walking robot with four arms. Basic control architecture is introduced for the robot to achieve walking motion and experimental results analyzed. Initial results show good agreement between the experimental results and simulation results.

We present a novel robot learning approach based on visual perception that allows a robot to acquire new skills by observing a demonstration from a tutor. Unlike most existing learning from demonstration approaches, where the focus is placed on the trajectories, in our approach the focus is on achieving a desired goal configuration of objects relative to one another. Our approach is based on visual perception which captures the object's context for each demonstrated action. This context is the basis of the visuospatial representation and encodes implicitly the relative positioning of the object with respect to multiple other objects simultaneously. The proposed approach is capable of learning and generalizing multi-operation skills from a single demonstration, while requiring minimum a priori knowledge about the environment. The learned skills comprise a sequence of operations that aim to achieve the desired goal configuration using the given objects. We illustrate the capabilities of our approach using three object reconfiguration tasks with a Barrett WAM robot.

Robot-assisted surgical procedures, such as Laser Phonomicrosurgery (LP), suffer from susceptibility to variation in surgeon skill and equipment characteristics. Ergonomic and human-centered approaches acquire increased importance in the design of surgeon-machine interfaces. This paper proposes a protocol for comparative evaluation of surgeon-machine interfaces based on two criteria: (i) the subjective evaluation of their usability using questionnaires, and (ii) the objective evaluation of their performance using an imaging-based feature extraction method. Two interfaces in LP, the traditional (“AcuBlade”) interface and the novel (“Virtual Scalpel”) interface, were evaluated to demonstrate the effectiveness of the proposed scheme. A series of experimental trials were conducted using the interfaces in surgery-like tasks in a controlled environment. The subjective evaluation pointed to the superiority of the Virtual Scalpel interface (score: 83.06) in terms of confidence and ease of use, and learnability, over the AcuBlade interface (score: 65.56). The objective evaluation showed the Virtual Scalpel interface having an overall score (55.96) significantly superior to the AcuBlade (51.37). It is thus shown that the multidimensional evaluation approach allowed to clearly distinguish between levels of perceived usability and effective performance of surgeon-machine interfaces from a user-centered perspective.

Gestures are characterized by intermediary or final landmarks (real or virtual) in task space or joint space that can change during the course of the motion, and that are described by varying accuracy and correlation constraints. Generalizing these trajectories in robot learning by imitation is challenging, because of the small number of demonstrations provided by the user. We present an approach to statistically encode movements in a task-parameterized mixture model, and derive an expectation-maximization (EM) algorithm to train it. The model automatically extracts the relevance of candidate coordinate systems during the task, and exploits this information during reproduction to adapt the movement in real-time to changing position and orientation of landmarks or objects. The approach is tested with a robotic arm learning to roll out a pizza dough. It is compared to three categories of task-parameterized models: 1) Gaussian process regression (GPR) with a trajectory models database; 2) Multi-streams approach with models trained in several frames of reference; and 3) Parametric Gaussian mixture model (PGMM) modulating the Gaussian centers with the task parameters. We show that the extrapolation capability of the proposed approach outperforms existing methods, by extracting the local structures of the task instead of relying on interpolation principles.

This paper proposes a control strategy for a compliant actuator, the CompAct™ actuator, which is equipped with semi active friction dampers in its transmission system. Both the transmission flexibility and the nonlinearity of the friction based damping torque makes the control of this actuator not a trivial task. This paper studies model of the presented actuator and the control problem of accurate link position tracking based on sliding mode approach that considers the friction torque as an uncertainty. Stability analysis and simulations highlight the effectiveness of the proposed controller in compensating for the deflections and unknown friction torque of the actuator. The performance of the controller is also validated by experiment results that demonstrate the tracking performance of the CompAct™ actuator achieved by the presented control strategy.

A legged robot has to deal with environmental contacts every time it takes a step. To properly handle these interactions, it is desirable to be able to set the foot compliance. For an actively-compliant legged robot, in order to ensure a stable contact with the environment the robot leg has to be passive at the contact point. In this work, we asses some passivity and stability issues of the actively-compliant leg of the quadruped robot HyQ, which employs a highperformance cascade compliance controller. We demonstrate that both the nested torque loop performance as well as the actuator bandwidth have a strong influence in the range of virtual impedances that can be passively rendered by the robot leg. Based on the stability analyses and experimental results, we propose a procedure for designing cascade compliance controllers. Furthermore, we experimentally demonstrate that the HyQ's actively-compliant leg is able to reproduce the compliant behavior presented by an identical but passively-compliant version of the same leg.

This paper presents the results of a multidisciplinary project where biologists, mechanical engineers and electronic engineers worked together to develop bio-inspired soft continuum arms, whose design captures and takes advantage of key features of the octopus anatomy and control. The cross-integration of such diverse expertise was channelled towards the design of soft continuum arms whose characteristics were inspired by nature, but with a focus on readily available engineering technologies and their effective integration from a system viewpoint. On one side the mechanical structure and the control was designed looking at the animal, in particular at the coupling between its anatomy and control system that allows the animal to survive in its ecosystem. On the other side engineering issues and constraints were carefully accounted for, namely material softness, intrinsic safety, energy efficiency, cost effectiveness and manufacturing aspects. The design evolution is presented through three different generations of prototypes where both bio-inspiration and engineering requirements are appropriately blended.

This paper presents a framework developed to increase the autonomy and versatility of a large (~75kg) hydraulically actuated quadrupedal robot. It combines onboard perception with two locomotion strategies, a dynamic trot and a static crawl gait. This way the robot can perceive its environment and arbitrate between the two behaviours according to the situation at hand. All computations are performed on-board and are carried out in two separate computers, one handles the high-level processes while the other is concerned with the low-level hard real-time control. The perception and subsequently the appropriate gait modifications are performed autonomously. We present outdoor experimental trials of the robot trotting over unknown terrain, perceiving a large obstacle, altering its behaviour to the cautious crawl gait and stepping onto the obstacle. This allows the robot to locomote quickly on relatively flat terrain and gives the robot the ability to overcome large irregular obstacles when required.

We describe the design and implementation of an on-line identification scheme for Autonomous Underwater Vehicles (AUVs). The proposed method estimates the dynamic parameters of the vehicle based on a global derivative-free optimization algorithm. It is not sensitive to initial conditions, unlike other on-line identification schemes, and does not depend on the differentiability of the model with respect to the parameters. The identification scheme consists of three distinct modules: a) System Excitation, b) Metric Calculator and c) Optimization Algorithm. The System Excitation module sends excitation inputs to the vehicle. The Optimization Algorithm module calculates a candidate parameter vector, which is fed to the Metric Calculator module. The Metric Calculator module evaluates the candidate parameter vector, using a metric based on the residual of the actual and the predicted commands. The predicted commands are calculated utilizing the candidate parameter vector and the vehicle state vector, which is available via a complete navigation module. Then, the metric is directly fed back to the Optimization Algorithm module, and it is used to correct the estimated parameter vector. The procedure continues iteratively until the convergence properties are met. The proposed method is generic, demonstrates quick convergence and does not require a linear formulation of the model with respect to the parameter vector. The applicability and performance of the proposed algorithm is experimentally verified using the AUV Girona 500.

Energy efficiency is one of the main challenges for long-term autonomy of AUVs (Autonomous Underwater Vehicles). We propose a novel approach for improving the energy efficiency of AUV controllers based on the ability to learn which external disturbances can safely be ignored. The proposed learning approach uses adaptive oscillators that are able to learn online the frequency, amplitude and phase of zero-mean periodic external disturbances. Such disturbances occur naturally in open water due to waves, currents, and gravity, but also can be caused by the dynamics and hydrodynamics of the AUV itself. We formulate the theoretical basis of the approach, and demonstrate its abilities on a number of input signals. Further experimental evaluation is conducted using a dynamic model of the Girona 500 AUV in simulation on two important underwater scenarios: hovering and trajectory tracking. The proposed approach shows significant energy-saving capabilities while at the same time maintaining high controller gains. The approach is generic and applicable not only for AUV control, but also for other type of control where periodic disturbances exist and could be accounted for by the controller.

This paper presents an integrated control framework for balancing humanoids on uneven terrains combining stabilization control and terrain inclination estimation. The stabilization is realized by passivity based admittance control that utilizes the force/torque feedback in feet to actively regulate the compliance. The logic-based terrain estimation algorithm exploits feet to probe the terrain inclination and deals with underactuation when feet tilt on the contact surface. The equilibrium position in the admittance control is thereby adapted for recovering balance on the slope. Both the theoretical work and experimental validation are presented. The method is implemented and validated on the real humanoid by demonstrating the capability of estimating terrain inclination, balancing on the slope with varying gradient, and maintaining upright posture in the meantime. Experimental data such as inclination estimation in the comparison study, center of pressure measurement, and body attitude compensation are presented and analyzed.

Robot programming by demonstration encompasses a wide range of learning strategies, from simple mimicking of the demonstrator's actions to the higher level extraction of the underlying intent. By focusing on this last form, we study the problem of extracting the reward function explaining the demonstrations from a set of candidate reward functions, and using this information for self-refinement of the skill. This definition of the problem has links with inverse reinforcement learning problems in which the robot autonomously extracts an optimal reward function that defines the goal of the task. By relying on Gaussian mixture models, the proposed approach learns how the different candidate reward functions are combined, and in which contexts or phases of the task they are relevant for explaining the user's demonstrations. The extracted reward profile is then exploited to improve the skill with a self-refinement approach based on expectation-maximization, allowing the imitator to reach a skill level that goes beyond the demonstrations. The approach can be used to reproduce a skill in different ways or to transfer tasks across robots of different structures. The proposed approach is tested in simulation with a new type of continuum robot (STIFF-FLOP), using kinesthetic demonstrations from a Barrett WAM manipulator.

Keeping balance is the main concern for humanoids in standing and walking tasks. This paper endeavors to acquire optimal ankle stabilization methods for humanoids with passive and active compliance and explain ankle balancing strategy from the compliance regulation perspective. Unlike classical stiff humanoids, the compliant ones can control both impedance and position during task operation. Optimal compliance regulation is resolved to maximize the stability of the humanoids. The linearized model is proposed to obtain the optimal ankle impedance for stabilizing against impacts. The nonlinear model is proposed as well and compared with the linear one. The proposed methods are validated by experiments on an intrinsically compliant humanoid using passivity based admittance and impedance controllers both in joint and Cartesian space.

This paper presents the application of a hydraulic DC-DC converter, namely a step down Buck Converter to the actuation of a robot leg that is part of the quadruped robot HyQ. The use of a Hydraulic Buck Converter (HBC) offers significant advantages in terms of improved efficiency of hydraulic actuation systems analogously to an electric switching DC-DC converter as opposed to a rheostatic-type system. In this paper, a HBC consisting of two digital valves and two check valves is introduced to improve the efficiency performance of a singl leg of a hydraulic quadruped robot (HyQ). This type of hydraulic buck converter is able to support the locomotion in two directions. The HBC operates at a switching frequency of 100 Hz in pulse-width-modulation. The better energy performance compared to proportional control is achieved by the use of fast check valves. The performance of the system with a 3-way-4-position proportional valve is compared with the HBC drive. A test rig is set up to investigate the performance of HBC with two different controllers and a Hydraulic Proportional Drive (HPD) system, based on proportional valves which control flow, by throttling it, in a dissipative manner. The performance of position tracking and energy consumption is evaluated. The experimental results indicate that HBC systems can achieve similar position tracking with relatively less consumed energy.

In this paper we describe an approach for hu-manoid robot balance recovery that combines a novel attitude control algorithm adding compliance to the robot's behavior and increasing the smoothness of its motion, and an omnidirectional stepping strategy that can trigger one or two steps based on a measured disturbance vector. The proposed method is validated through experiments with the inherently compliant humanoid COMAN.

This paper provides stability analyses for two different types of desired gravity compensation controllers, employing both motor and link feedback, and describes a means by which these controllers can be used to control a compliant humanoid robot in order to ensure the successful execution of walking trajectories. Given the challenging task of controlling compliant bipedal systems, owing to their possession of underactuated degrees of freedom, the full actuator and link dynamics are accounted for. The proposed walking strategy involves a process of switching between three distinct controllers which is contingent upon the force feedback provided by the force/torque sensors embedded in the robot's feet. These controllers were tuned using a simulation model of the robot and were then implemented on the compliant COMAN legs, whose performance of walking confirms the controllers' stability, in addition to the walking scheme's efficacy.

This paper presents the design of a novel compliant joint for high performance mobility. The design principle of the joint is based on an asymmetric compliant antagonistic scheme which is actuated by two motors of different power capability and efficiency. Torques from the two motors are transmitted to the joint through two elastic elements of different stiffness and energy storage capacity. The proposed compliant joint design combines high power performance, large energy storage capacity and physical resilience all necessary features for performing high performance mobility such as agile locomotion. The paper introduces the principle of operation, the design and mechanical implementation of the joint. Preliminary experimental trials demonstrate the joint performance in a single degree of freedom leg prototype system.

This paper presents a trajectory generator and an active compliance control scheme, unified in a framework to synthesize dynamic, feasible and compliant trot-walking locomotion cycles for a stiff-by-nature hydraulically actuated quadruped robot. At the outset, a CoP-based trajectory generator that is constructed using an analytical solution is implemented to obtain feasible and dynamically balanced motion references in a systematic manner. Initial conditions are uniquely determined for symmetrical motion patterns, enforcing that trajectories are seamlessly connected both in position, velocity and acceleration levels, regardless of the given support phase. The active compliance controller, used simultaneously, is responsible for sufficient joint position/force regulation. An admittance block is utilized to compute joint displacements that correspond to joint force errors. In addition to position feedback, these joint displacements are inserted to the position control loop as a secondary feedback term. In doing so, active compliance control is achieved, while the position/force trade-off is modulated via the virtual admittance parameters. Various trot-walking experiments are conducted with the proposed framework using HyQ, a ~ 75kg hydraulically actuated quadruped robot. We present results of repetitive, continuous, and dynamically equilibrated trot-walking locomotion cycles, both on level surface and uneven surface walking experiments.

Computer-assisted therapy is one of the most promising new techniques for those suffering from physical and neurological dysfunction. Yet, impairments to physical movement arising from a central nervous system dysfunction or from muscle spasms generated through other neurological damage or dysfunction can often make it difficult or impossible for individuals to interact with computer-generated environments using a conventional mouse interface. This paper investigates the use of a 2-D haptic device as an assistive robotic aid to minimize the effects of the pathological absence of motor control in the upper limb in impaired users when using a mouse. The assistive functionality is evaluated in 2-D tracking tasks using a human subject with failure of the gross coordination of the upper limb muscle movements-“Muscle Ataxia.” The results demonstrate that with this system the capability of the impaired subject to track predefined trajectories within a computer generated 2D is significantly improved. The average of the means of the error distance for the trajectories performed under the assistive mode was significantly lower (more than 40%) than that of the trajectories without assistance. In addition, when using the assistive device, the impaired subject was able to complete the tracking tasks in less time.

This paper is concerned with the design of a control strategy for variable stiffness actuators in series configuration, exploiting the lever concept to adjust the stiffness at the transmission. A control strategy based on gain scheduling is proposed, which is able to regulate both stiffness and position at output link. The gain scheduling is designed based on a set of linear quadratic regulators (LQRs), because LQR's inherent robustness properties can accommodate significant variation in the actuation plant parameters. The link positioning relies on continuous adjustment of the control effort based on the current transmission stiffness; the stiffness perceived at the output link is regulated through combined action of the transmission stiffness and the positioning gains of the scheduling strategy. The effectiveness of the controller is verified in simulation and experiments on the actuator with adjustable stiffness. The overall strategy has been proven to be locally stable.

Conventional rigid body robots typically use few degrees of freedom (DOF). This results in a manipulator that lacks flexibility and maneuverability when compared to continuum robots that utilize a much higher number of DOF. However, due to their continuous nature, the difficulty of measuring and controlling a large number of actuated DOF, and their high degree of nonlinearity, the development of control algorithms for continuum robot manipulators is an ongoing challenge. This paper presents an algorithm inspired by biological solutions from live octopus that utilizes division of functionality to achieve simple and robust control of continuum arm based systems. Simulated results for single and multiple dynamic continuum arms show the controller is capable of producing motions similar to that of octopus. The resulting controller is also computationally efficient enough for real-time implementation. In future this work will be implemented on a prototype robot with multiple continuum arms.

Legged robots have the potential to serve as versatile and useful autonomous robotic platforms for use in unstructured environments such as disaster sites. They need to be both capable of fast dynamic locomotion and precise movements. However, there is a lack of platforms with suitable mechanical properties and adequate controllers to advance the research in this direction. In this paper we are presenting results on the novel research platform HyQ, a torque controlled hydraulic quadruped robot. We identify the requirements for versatile robotic legged locomotion and show that HyQ is fulfilling most of these specifications. We show that HyQ is able to do both static and dynamic movements and is able to cope with the mechanical requirements of dynamic movements and locomotion, such as jumping and trotting. The required control, both on hydraulic level (force/torque control) and whole body level (rigid model based control) is discussed.

This paper presents an improved mode shape function-based 3D dynamic model for pneumatic muscle actuated continuum arms, and validates the model and simulation results through experimental testing. The model also facilitates the direct control of pneumatic muscle actuated continuum arms through the use of input pressure. This is achieved without additional intermediary transformations and does not have singularity problems present in previous models. The proposed arm model uses a new pneumatic muscle actuator (PMA) dynamic model with hysteresis that is capable of modelling both extending and contracting PMAs. The proposed hysteric model is simple, easily adaptable, and validated experimentally. The PMA model can be applied to dynamically model any PMA based system as well as PMA actuated continuum arms utilizing different actuator configurations.

This paper examines the design and control of a robotic arm inspired by the anatomy and neurophysiology of Octopus vulgaris in light of embodiment theory. Embodiment in an animal is defined as the dynamic coupling between sensorymotor control, anatomy, materials, and the environment that allows for the animal to achieve effective behaviour. Octopuses in particular are highly embodied and dexterous animals: their arms are fully flexible, can bend in any direction, grasp objects and modulate stiffness along their length. In this paper the biomechanics and neurophysiology of octopus have been analysed to extract relevant information for use in the design and control of an embodied soft robotic arm. The embodied design requirements are firstly defined, and how the biology of the octopus meets these requirements presented. Next, a prototype continuum arm and control architecture based on octopus biology, and meeting the design criteria, are presented. Finally, experimental results are presented to show how the developed prototype arm is able to reproduce motions performed by live octopus for contraction, elongation, bending, and grasping.

Variable stiffness actuators have been developed based on different design solutions which can be arranged into two groups: antagonistic and series design. In both the cases two actuation units are combined with passive elastic elements to adjust both the stiffness and the equilibrium position of the actuated joint. To regulate the stiffness, mechanical work is required to be done which depending on the design principle of the actuator results in certain energy consumption. In this paper different variable stiffness design approaches with different types of springs (linear, quadratic, exponential and cubic) are analyzed and compared with respect to the energy required to regulate the stiffness. The results give some insights about the design parameters which mostly affect the energy consumption for the stiffness adjustment. In this work, it is shown that among different design and spring arrangements, the variable stiffness in series design which uses linear springs with constant pretension, requires the minimum energy consumption to adjust the stiffness.

This work introduces a walking pattern generator suitable for humanoids with inherent joint compliance. The proposed walking pattern generator computes the desired center of mass (COM) references on-line based on the COM state feedback. The position and velocity of the COM are the feedback variables, and the constraint ground reaction force (GRF), which is limited by the support polygon, is the control effort to drive the COM states to track the desired ones. The zero moment point (ZMP) is obtained naturally as a result of GRF interaction with robot feet. The proposed COM tracking scheme demands a lower bandwidth from the controller compared to the ZMP tracking schemes. Experimental data of the real compliant humanoid, such as ZMP, COM motion, and GRF are presented to demonstrate the validation of the proposed gait generation method.

The work presents the standing stabilization of a compliant humanoid robot against external force disturbances and variations of the terrain inclination. The novel contribution is the proposed control scheme which consists of three strategies named compliance control in the transversal plane, body attitude control, and potential energy control, all combined with the intrinsic passive compliance in the robot. The physical compliant elements of the robot are exploited to react at the first instance of the impact while the active compliance control is applied to further absorb the impact and dissipate the elastic energy stored in springs preventing the high rate of spring recoil. The body attitude controller meanwhile regulates the spin angular momentum to provide more agile reactions by changing body inclination. The potential energy control module constrains the robot center of mass (COM) in a virtual slope to convert the excessive kinetic energy into potential energy to prevent falling. Experiments were carried out with the proposed balance stabilization control demonstrating superior balance performance. The compliant humanoid was capable of recovering from external force disturbances and moderate or even abrupt variations of the terrain inclination. Experimental data such as the impulse forces, real COM, center of pressure (COP) and the spring elastic energy are presented and analyzed.

Research in humanoid robotics in recent years has led to significant advances in terms of the ability to walk and even run. Yet, despite the general achievements in locomotion and control, energy efficiency is still one important area that requires further attention, especially as it is one of the major steeping stones leading to increased autonomy. This paper examines, and quantifies, the energetic benefits of introducing passive compliance into bipedal locomotion using COMAN, an intrinsically COmpliant huMANoid robot. The novelty of the method proposed consists of: i) the use of a method of gait synthesis based on kinematic Motion Primitives (kMPs) extracted from human, ii) the frequency tuning of the resultant trajectories, to excite the physical elasticity of the system, and the subsequent analysis of the energetic performance of the robot. The motivation is to assess the possible effects of using dynamic human-like, and human derived, trajectories, with significant Center of Mass (CoM) vertical displacement, regulated in frequency around the frequency band of the system resonances, on the excitation of the compliant actuators, and subsequently to measure and verify any energetic benefit. Experimental results show that if the gait frequency is close to one of the main resonant frequencies of the robot, then the total work contribution of the elastic compliant element to the overall motion of the robot is positive (15% of the work required is generated by the springs).

Haptic feedback should integrate kinaesthetic and tactile feedback. However current haptic displays do not satisfy the stringent performance and design requirements for integration in teleoperation and VR. This work presents the development of a compact, high performance tactile display for the fingertip. The compact design, high performance, reliability, and simple connectivity of this display make it suitable for immediate integration in current VR and master-slave haptic systems. In terms of performance this display achieves an excellent combination of force, amplitude and spatiotemporal resolution at the tactors, surpassing the performance of devices of a similar footprint. Its operation is based on the display of surface shape to an area of the fingertip through a 4×4 array of vertically moving tactors. The tactors are spring loaded and are actuated remotely by dc motors through a flexible tendon transmission. This work presents the overall design, control and performance of the device. A preliminary analysis of the transmission system is presented and is used to compensate for output errors induced by component elasticity.

Variable stiffness actuators (VSAs) have been introduced to improve, at the design level, the safety and the energy efficiency of the new generation of robots that have to interact closely with humans. A wide variety of design solutions have recently been proposed, and a common factor in most of the VSAs is the introduction of a flexible transmission with varying stiffness. This, from the control perspective, usually implies a nonlinear actuation plant with varying dynamics following time-varying parameters, which requires more complex control strategies with respect to those developed for flexible joints with a constant stiffness. For this reason, this paper proposes an approach for controlling the link position and stiffness of a VSA. The link positioning relies on a LQR-based gain scheduling approach useful for continuously adjusting the control effort based on the current stiffness of the flexible transmission. The stiffness perceived at the output link is adjusted to match the varying task requirements through the combination of the positioning gains and the mechanical stiffness. The stability of the overall strategy is briefly discussed. The effectiveness of the controller in terms of tracking performance and stiffness adjustment is verified through experiments on the Actuator with Adjustable Stiffness (AwAS).

It is known that bipedal robots with passive compliant structures have obvious advantages over stiff robots, as they are able to handle the potential energy management. Therefore, this paper is aimed at presenting a jumping pattern generation method that takes advantage of this property via the utilization of the base resonance frequency, which is of special importance. To begin with, the resonance frequency is determined through a system identification procedure on our actual robot. Consequentially, the vertical component of the CoM is generated via a periodic function in which the resonance frequency is employed. The horizontal component of the CoM is obtained using the ZMP criterion to guarantee the dynamic balance. Having obtained the necessary elements of the CoM trajectory within an analytical manner, joint motions are computed with the help of translational and angular momenta constraints. In order to validate the method, two legged jumping experiments are conducted on our actual compliant robot. In conclusion, we observed repetitive, continuous, and dynamically equilibrated jumping cycles with feasible landing phases.

Continuum structures with a very high or infinite number of degrees of freedom (DOF) are very interesting structures in nature. Mimicking this kind of structures artificially is challenging due to the high number of required DOF. This paper presents a kinematic and dynamic model for an underwater robotic manipulator inspired by Octopus vulgaris. Then, a prototype arm inspired by live octopus is presented and the model validated experimentally. Initial comparisons of simulated and experimental results show good agreement.

A summary of the state-of-the-art reinforcement learning in robotics is given, in terms of both algorithms and policy representations. Numerous challenges faced by the policy representation in robotics are identified. Two recent examples for application of reinforcement learning to robots are described: pancake flipping task and bipedal walking energy minimization task. In both examples, a state-of-the-art Expectation-Maximization-based reinforcement learning algorithm is used, but different policy representations are proposed and evaluated for each task. The two proposed policy representations offer viable solutions to four rarely-addressed challenges in policy representations: correlations, adaptability, multi-resolution, and globality. Both the successes and the practical difficulties encountered in these examples are discussed.

Soft robots are difficult to control because of their compliant and elastic body dynamics compared with robots made of rigid bodies. In this paper, we present a control scheme inspired by the octopus called timing-based control for soft robotic arms. This control scheme is motivated to positively exploit the natural dynamics of the soft body. We demonstrate a scheme for controlling an object-reaching task by using an echo state network on a 3D physical soft robotic arm simulator and show that this network can successfully perform the task. Detailed analyses and evaluations of the generalization capacity of the network and the performances to the reaching task are presented.

This paper reports on the modelling and trajectory generation of an intrinsically compliant humanoid robot. To achieve adequate gait tracking performance in a compliant robot is not trivial and cannot be addressed with the traditional control approaches used for stiff robots. To permit the development of effective gait generators which take into account the additional dynamic effects due to intrinsic compliance, an appropriate model which can predict the robot motion dynamics is required. In this work, we propose a model which combines the inverted pendulum model approach with a compliant model (Cartesian) at the level of the COM. Based on this model which permits to predict the motion of the centre of mass (COM) of the compliant robot an Internal Model Control strategy is adopted to improve the gait tracking performance. The derivation of the model is introduced followed by experimental validation which demonstrates the tracking performance achieved by the proposed reduced model. The Internal Model Control is subsequently discussed and validated on the COmpliant huMANoid COMAN using a series of ZMP based walking gaits.

Robots are frequently modeled as rigid body systems, having torques as input to their dynamics. A high-performance low-level torque source allows us to control the robot/environment interaction and to straightforwardly take advantage of many model-based control techniques. In this paper, we define a general 1-DOF framework, using basic physical principles, to show that there exists an intrinsic velocity feedback in the generalized force dynamics, independently of the actuation technology. We illustrate this phenomena using three different systems: a generic spring-mass system, a hydraulic actuator, and an electric motor. This analogy helps to clarify important common aspects regarding torque/force control that can be useful when designing and controlling a robot. We demonstrate, using simulations and experimental data, that it is possible to compensate for the load motion influence and to increase the torque tracking capabilities.

Controllers for articulated robots such as an arm or a humanoid commonly need to continuously calculate complex algebraic quantities, such as the joint space inertia matrix or Jacobians. An effective and fast implementation of the calculation of these quantities is crucial to achieve complex, yet robust controllers and thus enable sophisticated behaviors in robots. Although the nature of these algebraic quantities is very well known in robotics, they do not lend themselves easily to manual implementation, because of ambiguities and the complexity in their development and use. We propose an approach that addresses this issue by relying on automatic code generation, thus relieving the user from hand crafted development. Our approach also addresses efficiency and speed, in order to satisfy the strict requirements of real time robot controllers, yet it is easy to use. We show the effectiveness of our method by means of some preliminary comparisons.

This paper presents the kinematics, dynamics, and experimental results for a novel quadruped robot using continuum limbs. We propose soft continuum limbs as a new paradigm for robotic locomotion in unstructured environments due to their potential to generate a wide array of locomotion behaviors ranging from walking, trotting, crawling, and propelling to whole arm grasping as a means of negotiating difficult obstacles. A straightforward method to derive the kinematics and dynamics for the proposed quadruped has been demonstrated through numerical simulations. Initial experiments on a prototype continuum quadruped demonstrate the ability to stand up from a flat-belly stance, absorb external disturbances such as maintaining stability after dropping from a height and after being perturbed by a collision, and crawling on flat and cluttered environments. Experiment results provide evidence that locomotion with soft continuum limbs are feasible and usable in unstructured environments for variety of applications.

This paper presents a control algorithm to achieve crawling locomotion for a multi-arm robotic system inspired by live octopuses. First the paper introduces a dynamic model of a continuum arm. The model accounts for the key features relevant to crawling locomotion, namely longitudinal muscles and suckers that provide force interaction with the surrounding environment. This single arm model is then validated against live octopus data and expanded to an 8-arm system. Appropriate coordination algorithms of the eight arms result in crawling locomotion. The results of this work can be used to study the motor control schemes for both multiple continuum arm robots and live octopuses.

An automated approach is proposed which can analyze ground reaction force data from bipedal walking robots and humans. The input of the automated analysis is the raw data from force sensors mounted in the feet of a robot. The output is detailed information, such as detected single support, double support, and swing phases, their durations, timings of events like heel strikes, properties of the phase transitions and of the robot itself. The proposed approach is generic, parameter-free, model-free, robust, computationally efficient, and applicable for real-time use during walking. It can detect early indications of instability that could lead to a fall of the robot. Three real-world experiments are presented: with a compliant bipedal robot, with a stiff humanoid robot, and with a human subject.

Recently, compliance has been considered as one of the key physical properties that a robot should incorporate to be able to physically interact with humans and uncertain environments. Apart from the improved ability of interaction, mechanical robustness and higher safety-related performances, compliance introduces underdamped oscillatory modes and reduces the mechanical natural frequency of the plant to be controlled making its control much more complex than that of conventional stiff actuators. To overcome these drawbacks, some recent works focus on the incorporation of physical damping within compliant actuators. This work presents an analysis for the quantitative evaluation of the effects of physical damping in compliant robotic joints to demonstrate the improvements (dynamic performance, stability, controllability, tracking precision and energy efficiency) which can be gained by incorporating physical damping in such flexible transmission systems. Simulation and experimental results validate that these benefits can effectively be achieved on an existing compliant actuator prototype with variable physical damping.

Tactile feedback is a key modality in object exploration and manipulation. However it has not been satisfactorily addressed in master-slave teleoperation. One of the reasons is that current tactile displays do not satisfy the stringent performance and design requirements for integration in master haptic devices. This paper presents the integration of a compact, high performance tactile display in a teleoperation setup. In this scenario the tactile display provides tactile feedback while remote objects are being contacted through a soft robotic finger. The display is mounted on a force feedback master device, which controls a manipulator equipped with a force sensor and a soft finger. Force feedback and tactile feedback are combined in a hybrid approach. Force feedback on the master reflects the measured remote interaction forces while tactile feedback is model based. This hybrid feedback method can provide force and tactile information in cases of highly structured teleoperation scenes where tactile sensing is absent or rudimentary while accurate environment models exist. An experiment is presented where users teleoperated the robot finger in a 3D contour following task with and without tactile feedback. Subjects' performances indicate an improvement in teleoperation when both tactile and force feedback are present.

Most of today's robots have rigid structures and actuators requiring complex software control algorithms and sophisticated sensor systems in order to behave in a compliant and safe way adapted to contact with unknown environments and humans. By studying and constructing variable impedance actuators and their control, we contribute to the development of actuation units which can match the intrinsic safety, motion performance and energy efficiency of biological systems and in particular the human. As such, this may lead to a new generation of robots that can co-exist and co-operate with people and get closer to the human manipulation and locomotion performance than is possible with current robots.

Interfaces capable of providing tactile and force feedback are required to enhance interaction in VR applications, especially in those applications that involve the exploration of small geometric features and manipulation of small objects. This paper presents a high performance haptic interface capable of displaying tactile and force feedback information when interacting with virtual objects. The main objective of this work is to examine the capabilities of this interface to provide realistic sensations during the exploration of small surface features, such as ridges and convex shapes. To achieve this goal, two exploratory experiments under different feedback modalities were conducted. In the first experiment was evaluated the performance of users in detecting and localizing small convex shapes. In the second experiment was measured the users' ability to discriminate the angle of two chevron-shaped ridges. Results from the first experiment indicated a significant increase in performance not only when the interface provided tactile and force feedback but also when it provided only tactile feedback. Results from the second experiment showed that relatively small differences in angle (±7%) can be discriminated using the tactile and force feedback interface. An average threshold of 6.4° was obtained.

This paper investigates the tactile orientation discrimination of virtual, small-scale, patterned surfaces through a tactile display. The tactile display is an array of 4×4 vertically moving pins which can simulate the shape of small scale features. Its small size and low mass allow users to explore virtual surfaces freely and intuitively. The evaluation method consisted of measuring the orientation discrimination threshold for virtual surfaces with sinusoidal profiles. The threshold was compared with the threshold obtained using a non-actuated tactile display of equal spatial resolution and with the threshold obtained using the bare finger to explore real surfaces. This comparison was done to evaluate the quality of the rendered virtual surfaces, the performance of the device as a control system and the efficiency of the tactile rendering algorithm. This study also measured the speed and normal force during the tactile exploration that allowed investigation of the exploration strategies used when touching through normal indentation displays and through the bare finger. Furthermore they are the necessary interaction parameters for assessing the bandwidth and force output of the device.

This paper presents a new three dimensional kinematic and dynamic model for variable length continuum arm robotic structures using a novel shape function-based approach. The model incorporates geometrically constrained structure of the arm to derive its deformation shape function. It is able to simulate spatial bending, pure elongation, and incorporates a new stiffness control feature. The model is validated through numerical simulations, based on a prototype continuum arm, that yields physically accurate results.

In repetitive trajectories, adaptable compliance actuators can minimize energy consumption thanks to their ability to adjust the level of stiffness which allows the exploitation of the natural dynamics of their link based on the desired motion's frequency. However for most of these actuators in case of a variable frequency motion, it is not energetically beneficial to exploit the natural dynamics in the real time due to the considerably high amount of energy needed to change the stiffness. AwAS (Actuator with Adjustable Stiffness) achieves the stiffness regulation not through the control of the spring pretension (as in most of the existing variable stiffness joints) but by controlling the location of the spring elements. An important consequence of this mechanism is that the displacement needed to change the stiffness is perpendicular to the forces generated by the springs which in turn helps to minimize the energy/power required to regulate the stiffness. It is experimentally shown that AwAS is capable of minimizing energy consumption through exploiting the natural dynamics in real time for both fixed and variable frequency motions.

The Actuator with Adjustable Stiffness (AwAS) is an actuator which can independently control equilibrium position and stiffness by two motors. The first motor controls the equilibrium position while the second motor regulates the compliance. This paper describes the design and development of AwAS-II which is an improved version of the original realization. AwAS tuned the stiffness by controlling the location of the springs and adjusting its arm, length. Instead AwAS-II regulates the compliance by implementing a force amplifier based on a lever mechanism on which a pivot point can adjust the force amplification ratio from zero to infinitive. As in the first implementation, the actuator which is responsible for adjusting the stiffness in AwAS II is not working against the spring forces. Its displacement is perpendicular to the force generated by springs which makes changing the stiffness energetically efficient. As the force amplification ratio can theoretically change from zero to infinitive consequently the level of stiffness can tune from very soft to completely rigid. Because this range does not depends on the spring's rate and length of the lever, thus soft springs and small lever can be used which result in a lighter and more compact setup. Furthermore as the lever arm is shorter the time required for the stiffness regulation is smaller.

We present an integrated approach allowing a free-standing humanoid robot to acquire new motor skills by kinesthetic teaching. The proposed method controls simultaneously the upper and lower body of the robot with different control strategies. Imitation learning is used for training the upper body of the humanoid robot via kinesthetic teaching, while at the same time Reaction Null Space method is used for keeping the balance of the robot. During demonstration, a force/torque sensor is used to record the exerted forces, and during reproduction, we use a hybrid position/force controller to apply the learned trajectories in terms of positions and forces to the end effector. The proposed method is tested on a 25-DOF Fujitsu HOAP-2 humanoid robot with a surface cleaning task.

The new areas of technical exploitation of robotics systems has recently set new trends for the robotic actuation by demanding more versatile systems which can cope with unpredictable interactions within not well defined environments and work in close vicinity with the human. Following these trends, this work presents the development of a new actuation system with embodied characteristics such as passive compliance and variable physical damping. Compared to the other existing compliant linear or rotary actuators the proposed CompAct unit has the ability to regulate the oscillations induced by the introduction of the compliance by means of a variable physical damping actuator (VPDA) unit. Apart from facilitating the control the VPDA unit can assist in managing the energy transfer from/to the compliant module. The mechatronics, model and control scheme of the CompAct are analysed. The overall system is evaluated with experimental trials performed using a prototype unit. Preliminary results are presented to show that the unit and the proposed control scheme are capable of regulating the impedance components (stiffness and damping) within a wide range and with good fidelity.

The “iCub ”is a robotic platform that was developed by the RobotCub [1] consortium to provide the cognition research community with an open “child-like ”humanoid platform for understanding and development of cognitive systems [1]. In this paper we present the mechanical realization of the lower body developed for the “cCub ”humanoid robot, a derivative of the original “iCub”, which has passive compliance in the major joints of the legs. It is hypothesized that this will give to the robot high versatility to cope with unpredictable disturbance ranging from small uneven terrain variations to unexpected collisions or even accidental falls. As part of the AMARSI European project, the passive compliance of this newly developed robot will be exploited for safer interaction, energy efficient and more aggressive damage-safe learning. The passive compliant actuation module used is a compact unit based on the series elastic actuator principle (SEA). In addition to the passive compliance the “cCub ”design includes other significant updates over the original prototype such as full joint state sensing including joint torque sensing and improved range of motion and torque capabilities. In this paper, the new leg mechanisms of the “cCub ”robot are introduced.

Continuum robotic arms are based on non-rigid components that result in a nearly infinite number of degrees of freedom (DOF). Due to this reason it can be very complex to establish mathematical models for continuum robotic arms. This paper presents a 3D dynamic model of an arm based on octopus anatomy that utilizes 4 longitudinal and 4 radial muscles. The arm is composed of a multi-segment structure having distributed stiffness and damping to represent the muscles. The simulations are applied to a multi-segment arm, and results mimic several typical octopus arm motions.

We study the use of different weighting mechanisms in robot learning to represent a movement as a combination of linear systems. Kinesthetic teaching is used to acquire a skill from demonstrations which is then reproduced by the robot. The behaviors of the systems are analyzed when the robot faces perturbation introduced by the user physically interacting with the robot to momentarily stop the task. We propose the use of a Hidden Semi-Markov Model (HSMM) representation to encapsulate duration and position information in a robust manner with parameterization on the involvement of time and space constraints. The approach is tested in simulation and in two robot experiments, where a 7 DOFs manipulator is taught to play a melody by pressing three big keys and to pull a model train on its track.

This paper presents a new three dimensional (3D) kinematic model based on mode shape functions (MSF) for multisection continuum arms. It solves the singularity problems associated with previous models and introduces a novel approach for intuitively deriving exact, singularity-free MSFs, thus avoiding mode switching schemes and simplifying error models. The model is able to simulate spatial bending, pure elongation/contraction, and introduces inverse orientation kinematics for the first time to multisection continuum arms. Also, it carefully accounts for physical constraints in the joint space to provide enhanced insight into practical mechanics, and produces correct results for both forward and inverse kinematics. The model is validated through simulations, based on a prototype continuum robotic arm. Proposed approach is applicable to a broad spectrum of continuum robotic arm designs.

The octopus arm is a unique tool that combines strength and flexibility. It can shorten, elongate and bend at any point along its length. To model this behavior, a hyper-redundant manipulator composed of multiple segments is proposed. Each segment is a parallel robotic mechanism with redundant actuation. The kinematics and dynamics of this manipulator are analyzed and simulated utilizing a modular computational modeling method. Simulation results for some primitive movements are presented, and the effect of hydrodynamic forces is included.

We present a learning-based approach for minimizing the electric energy consumption during walking of a passively-compliant bipedal robot. The energy consumption is reduced by learning a varying-height center-of-mass trajectory which uses efficiently the robot's passive compliance. To do this, we propose a reinforcement learning method which evolves the policy parameterization dynamically during the learning process and thus manages to find better policies faster than by using fixed parameterization. The method is first tested on a function approximation task, and then applied to the humanoid robot COMAN where it achieves significant energy reduction.

A medical robotic system for teleoperated laser microsurgery based on a concept we have called “virtual scalpel” is presented in this paper. This system allows surgeries to be safely and precisely performed using a graphics pen directly over a live video from the surgical site. This is shown to eliminate hand-eye coordination problems that affect other microsurgery systems and to make full use of the operator's manual dexterity without requiring extra training. The implementation of this system, which is based on a tablet PC and a new motorized laser micromanipulator offering 1μm aiming accuracy within the traditional line-of-sight 2D operative space, is fully described. This includes details on the system's hardware and software structures and on its calibration process, which is essential for guaranteeing precise matching between a point touched on the live video and the laser aiming point at the surgical site. Together, the new hardware and software structures make both the calibration parameters and the laser aiming accuracy (on any plane orthogonal to the imaging axis) independent of the target distance and of its motions. Automatic laser control based on new intraoperative planning software and safety improvements based on virtual features are also described in this paper, which concludes by presenting results from sets of path following evaluation experiments conducted with 10 different subjects. These demonstrate an error reduction of almost 50% when using the virtual scalpel system versus the traditional laser microsurgery setup, and an 80% error reduction when using the automatic laser control routines, evidencing great improvements in terms of precision and controllability, and suggesting that the technological advances presented herein will lead to a significantly enhanced capacity for treating a variety of internal human pathologies.

When collaboration between human users and robots involves physical interaction, the importance of the safety issue arises. We propose a method to transfer to robots several tasks demonstrated by the user through kinesthetic teaching and subsequently learned using a weighted combination of dynamical systems (DS). The approach used to encode the desired skills ensures a safe robot behavior during the task reproduction, allowing physical interaction with the user who can employ the manipulator as a tangible interface. By using a force sensor-less impedance controller with a back-drivable robot, this concept is exploited in two physical human-robot interaction (pHRI) scenarios. The first considers an emergency situation in which the user can stop or pause a task execution by grasping and moving the robot away from the region of space associated to the skill. The second studies the possibility to select one among several learned tasks and switch to its execution by physically guiding the robot towards the task region.

A novel revolute nonlinear series elastic actuator called the HypoSEA is presented. The actuator uses a hypocycloid mechanism to stretch a linear spring in a nonlinear way. The actuator is optimized for highly dynamic tasks such as running and jumping, as it features a 120Nm torque capability and more than 30J of passive energy storage. When combined with a suitable controller, using the spring as an energy buffer can greatly reduce the work done by the rotor during periodic motions. The design has exceptionally low reflected mechanical impedance, making it robust against repeated impact loads. The nonlinear stiffening spring is optimized for the nonlinearities typically found in revolute-jointed hopping robots, and may be adjusted offline using a pretensioning mechanism. Finally, the low effective stiffness around the zero-torque equilibrium allows for extremely sensitive force control.

This paper describes the design and modelling of a new variable stiffness actuator (CompAct-VSA). The principle of operation of CompAct-VSA is based on a lever arm mechanism with a continuously regulated pivot point. The proposed concept allows for the development of an actuation unit with a wide range of stiffness and a fast stiffness regulation response. The implementation of the actuator makes use of a cam shaped lever arm with a variable pivot axis actuated by a rack and pinion transmission system. This realization results in a highly integrated and modular assembly. Size and weight are indeed an open issue in the VSAs design, which ultimately limit their implementation in multi-dof robotic systems. The paper introduces the mechanics, the principle of operation and the model of the actuator. Preliminary results are presented to demonstrate the fast stiffness regulation response and the wide range of stiffness achieved by the proposed CompAct-VSA design.

This work presents a quantitative evaluation of subjects' tactile ability to discriminate small virtual ridge patterns through a portable-wearable tactile device. The virtual patterns have been recreated by controlling the vertically moving pins of the device. Psychophysical experiments were performed to measure subjects' thresholds for spatial variation discrimination of ridge patterns. Moreover, for comparison reasons, further psychophysical experiments were performed with real ridge patterns using a non-actuated version of the tactile device and touching directly with the bear finger. During experiments, the exploration velocity was monitored. The present results help to understand, compare and characterize the tactile display when rendering small ridge patterns. The output of the presented study can also assist in the development of new tactile systems.

This paper presents a mixed-reality system for the training of operators (biologogist/neuroscientists) on fully teleoperated biomanipulations. These tasks are traditionally performed via direct manual control of the biomanipulation equipment while looking through the binoculars of a microscope. However, direct manual control makes the conventional systems susceptible to even very small operator errors, and extensive training is normally required to attain a satisfactory proficiency. To improve this area, a fully teleoperated biomanipulation system has been previously developed, but efficient operation of that system also requires some training. Therefore, the system presented here has been created to help new operators became familiar with the teleoperated system environment, introducing them to the system controls and joysticks functions. Two mixed-reality training games were designed, implemented and tested for this purpose: A ¿move-and-shoot¿ game focused on precise positioning training; and a trajectory following game intended to develop precise motion control skills on new operators. Preliminary experiments performed with 20 totally novice operators demonstrated that this new training system is effective in terms of the initial development of control skills for real teleoperated biomanipulations. Experimental metrics demonstrated an exponential learning curve for these novice operators, who achieved good performance values after only two practice runs on the system. In addition, training here was proven safe and inexpensive since no real cells, biochemical products, or several pipettes were needed for this initial training phase.

Most humanoid robots walk with bent knees, which particularly requires high motor torques at knees and gives an unnatural walking manner. It is therefore essential to design a control method that produces a motion which is more energy efficient and natural comparable to those performed by humans. In this paper, we address this issue by modeling the virtual spring-damper based on the cart-table model. This strategy utilizes the preview control, which generates the desired horizontal motion of the center of mass (COM), and the virtual spring-damper for generating the vertical COM motion. The theoretical feasibility of this hybrid strategy is demonstrated in Matlab simulation of a multi-body bipedal model. Knee joint patterns, ground reaction force (GRF) patterns, COM trajectories are presented. The successful walking gaits of the child humanoid "iCub" in the dynamic simulator validate the proposed scheme. The joint torques required by the proposed strategy are reduced, compared with the one required by the cart-table model.

This paper shows a novel application of the diffusion tensor for anisotropic image processing. The designed system aims at spotting and localizing injection points on a population of adherent cells lying on a Petri's dish. The overall procedure is described including pre-filtering, ridge enhancement, cell segmentation, shape analysis and injection point detection. The anisotropic contour completion (ACC) employed is equivalent to a dilation with a continuous elliptic structural element that takes into account the local orientation of the contours to be closed, preventing extension towards the normal direction. Experiments carried out on real images from an optical microscope revealed a remarkable reliability with up to 86% of cells in the field of view correctly segmented and targeted for microinjection.

We present an approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) to learning robust models of human motion through imitation. The proposed approach allows us to extract redundancies across multiple demonstrations and build time-independent models to reproduce the dynamics of the demonstrated movements. The approach is systematically evaluated by using automatically generated trajectories sharing similarities with human gestures. The proposed approach is contrasted with four state-of-the-art methods previously proposed in robotics to learn and reproduce new skills by imitation. An experiment with a 7 DOFs robotic arm learning and reproducing the motion of hitting a ball with a table tennis racket is then presented to illustrate the approach.

This paper is focussed on the modelling and control of a hydraulically-driven biologically-inspired robotic leg. The study is part of a larger project aiming at the development of an autonomous quadruped robot (hyQ) for outdoor operations. The leg has two hydraulically-actuated degrees of freedom (DOF), the hip and knee joints. The actuation system is composed of proportional valves and asymmetric cylinders. After a brief description of the prototype leg, the paper shows the development of a comprehensive model of the leg where critical parameters have been experimentally identified. Subsequently the leg control design is presented. The core of this work is the experimental assessment of the pros and cons of single-input single-output (SISO) vs. multiple-input multiple-output (MIMO) and linear vs. nonlinear control algorithms in this application (the leg is a coupled multivariable system driven by nonlinear actuators). The control schemes developed are a conventional PID (linear SISO), a Linear Quadratic Regulator (LQR) controller (linear MIMO) and a Feedback Linearisation (FL) controller (nonlinear MIMO). LQR performs well at low frequency but its behaviour worsens at higher frequencies. FL produces the fastest response in simulation, but when implemented is sensitive to parameters uncertainty and needs to be properly modified to achieve equally good performance also in the practical implementation.

This paper introduces the development of a semi-active friction based variable physical damping actuator (VPDA) unit. The realization of this unit aims to facilitate the control of compliant robotic joints by providing physical variable damping on demand assisting on the regulation of the oscillations induced by the introduction of compliance. The mechatronics details and the dynamic model of the damper are introduced. The proposed variable damper mechanism is evaluated on a simple 1-DOF compliant joint linked to the ground through a torsion spring. This flexible connection emulates a compliant joint, generating oscillations when the link is perturbed. Preliminary results are presented to show that the unit and the proposed control scheme are capable of replicating simulated relative damping values with good fidelity.

This paper presents the control architecture and preliminary experimental results of a high performance parallel robot used for ankle rehabilitation. The goal of this work was to design suitable control algorithms for diagnostic, training and rehabilitation of the ankle in presence of musculoskeletal injuries. A position control scheme is used for patient-passive exercises while an admittance control technique is used to perform patient-active exercises with and without motion assistance. The design of the control algorithms is based on the analysis of the rehabilitation protocol taking into account the dynamics of the system and the dynamics of the interaction between the human and the robot. Electromyographic (EMG) signals are used to evaluate patient's effort during training/exercising. The results indicate the great potential of the rehabilitation device as a tool to fasten and improve the ankle therapies outcome.

We propose a control strategy for a robotic manipulator operating in an unstructured environment while interacting with a human operator. The proposed system takes into account the important characteristics of the task and the redundancy of the robot to determine a controller that is safe for the user. The constraints of the task are first extracted using several examples of the skill demonstrated to the robot through kinesthetic teaching. An active control strategy based on task-space control with variable stiffness is proposed, and combined with a safety strategy for tasks requiring humans to move in the vicinity of robots. A risk indicator for human-robot collision is defined, which modulates a repulsive force distorting the spatial and temporal characteristics of the movement according to the task constraints. We illustrate the approach with two human-robot interaction experiments, where the user teaches the robot first how to move a tray, and then shows it how to iron a napkin.

The human foot serves three main functions; adapts to contours of ground, absorbs shock impacts and stores and releases energy. Despite significant development of humanoid robots the foot has seen little research and as a result most bipeds do not walk in a humanlike manner. In this work a study of the human foot is conducted to determine how each of these is achieved. The paper then describes the design, construction and testing of a fully articulated humanoid foot which has the same functionality as the human foot.

This paper deals with a comparative study on using water and air as actuation means for the control of a fluidic muscle (designed for air) and assesses the performance, particularly from a dynamic and energetic point of view. A medium with higher bulk modulus such as oil/water is believed to increase pressure and force bandwidths and reduce sensitivity to load variations, as is the case with conventional hydraulic stiff actuation systems. However in this application the inherent flexibility of the muscle plays a major role. Water has been chosen because of its non-flammability, environmental friendliness and the low solubility of air in it. The operating pressure range of the pneumatic muscle is 0-6 bar (typical range of a pneumatic system) that is well below typical operating pressures of hydraulic systems (typically over 100 bar). At such low pressures the dynamic behaviour of water is less predictable because of the higher likelihood of entrapped air in the water which physically occurs when operating at low pressures. This can majorly affect water bulk modulus and hence its dynamic performance. Therefore, the behaviour of the system in this unconventional pressure range for a liquid must be more thoroughly investigated. Theoretical and experimental analyses on a dedicated test rig have been carried out to assess these assumptions.

This paper is concerned with the application of switching technology to hydraulic actuation. Over the last 50 years with advances in power electronics, faster and faster static switches have been developed and applied to the control of motors. Hydraulic technology evolved in the opposite direction: switching control was not considered, and more and more accurate proportional flow/pressure control devices (servovalves etc) were developed. However despite the sophistication of such valves, from an energetic viewpoint proportional control is dissipative and inefficient. Indeed, by analogy it can be seen as the equivalent of resistive (rheostatic) motor control. In robotic applications where high power density, ruggedness and reliability are key requirements hydraulic actuation can be a sensible choice. However, the low efficiency of proportional control can be a limitation and it is necessary to go beyond the paradigm of proportional flow/pressure control. One response to this challenge is to revisit traditional on-off hydraulic technology and develop "power hydraulic" devices that behave in analogous manner to their power electronic counterparts. "Power hydraulics" is a challenging and little explored technology due to the markedly non-linear behaviour of hydraulic systems and the need of components with dynamic specifications that are not readily available off-the-shelf. After an analysis of the real on-off characteristics of a valve, a prototype hydraulic switching converter, inspired by the electric DC-DC Buck converter, is presented and its performance in pressure control mode, relative to a classical proportional valve-controlled system, are assessed. An energy saving of 75% is achieved. Merits and limitations of the current design are identified.

This paper focuses on the design of a robotic arm inspired to the anatomy and morphology of an octopus arm. The octopus is a boneless animal and its amazing dexterity is due to its muscular structure where longitudinal (axial), transverse (radial) and oblique muscles seamlessly interact while preserving hydrostaticity i.e. volume conservation (“muscular hydrostat”). Mimicking some features of the octopus is instrumental to design a dexterous and compliant system. After analysing the relevant anatomical and morphological characteristics of the octopus arm, the key biomechanical features of interest to the design of a robotic arm have been identified. A design methodology has been developed based on the analysis of the muscular hydrostat properties. A prototype arm has been built using bespoke contracting pneumatic muscles and expanding elements. In the current stage of development the system has 15 actuated degrees of motion (DOM) and 8 degrees of freedom (DOF), all independently controllable through valves and a dedicated electronics and software interface. Pros and cons of the current design as well as practical prototyping trade-offs are thoroughly described.

This paper describes the design and development of a new actuator with adjustable stiffness (AwAS) which can be used in robots which are necessary to work close to or physically interact with humans, e.g. humanoids and exoskeletons. The actuator presented in this work can independently control equilibrium position and stiffness by two motors. The first motor controls the equilibrium position while the second motor regulates the compliance. The novelty of the proposed design with respect to the existing systems is on the principle used to regulate the compliance. This is done not through the tuning of the pretension of the elastic element as in the majority of existing system but by controlling the fixation of the elastic elements (springs) using a linear drive. An important consequence of this approach is that the displacement needed to change the stiffness is perpendicular to the forces generated by the springs, thus this helps to minimize the energy/power required to change the stiffness. This permits the use of a small motor for the stiffness adjustment resulting in a lighter setup. Experimental results are presented to show the ability of AwAS to control position and regulate the stiffness independently.

We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. Expectation-Maximization based Reinforcement Learning is then used to modulate the mixture of dynamical systems initialized from the user's demonstration. The approach is evaluated on a torque-controlled 7 DOFs Barrett WAM robotic arm. Two skill learning experiments are conducted: a reaching task where the robot needs to adapt the learned movement to avoid an obstacle, and a dynamic pancake-flipping task.

The current research effort in the design of lightweight and safe robots is resulting in increased interest for the development of variable stiffness actuators. Antagonistic pneumatic muscle actuators (pMAs) have been proposed for this purpose, due to their inherent nonlinear spring behavior resulting from both air compressibility and their nonlinear force-length relation. This paper addresses the simultaneous torque and stiffness control of an antagonistically actuated joint with pneumatic muscles driven by compact, fast-switching solenoid valves. This strategy allows compensation of unmodeled joint dynamics while adjusting the joint stiffness depending on the task requirements. The proposed controller is based on a sliding mode force control applied to an average model of the valve-pneumatic muscle system. This was necessary to cope with both the well known model uncertainties of the pMA and the discontinuous on-off behavior of the solenoid valves. Preliminary experimental results verified the effectiveness of the proposed implementation.

This paper focuses on the design and experimental evaluation of a hydraulically actuated robot leg. The evaluation of the leg prototype is an important milestone in the development of HyQ, a Hydraulically actuated Quadruped robot. The prototype features two rotary joints actuated by hydraulic cylinders and has a mass of 4.5kg. We performed several experiments with the leg prototype attached to a vertical slider to tests the robustness of the mechanical design and the hydraulic actuation system. Besides the experimental evaluation of the hydraulic components, we also extensively studied the sensor data of the leg during periodic hopping. The results show that hydraulic actuation is suitable for legged robots because of its high power-to-weight ratio, fast response and ability to cope with high impact force peaks. Furthermore, we compare the cylinder force data obtained by the load cell with the calculated value based on the cylinder pressures to analyze if it is possible to eliminate this sensory system redundancy in the future. Through these studies, weaknesses of the design were identified and suggestions on how to improve them are presented.

Hydraulic actuators are characterized by fast dynamics, high power density, high stiffness, large output force/torque, and in recent years are becoming increasingly attractive in the field of robotics. This paper presents the study of a 3-way proportional valve controlled, spring assisted electro-hydraulic rotary actuator, which consists of a 3-way proportional valve, a linear cylinder and a reciprocal spring. The operating principle is presented and a mathematical model is developed. Comparison analysis is made between the new actuator and a traditional one with an application to a hydraulically actuated legged robot.

Interventional radiology is a rapidly expanding speciality using minimally invasive techniques to treat a multitude of clinical problems. Current work in progress aims to create an affordable virtual training tool to reduce training times and patient risk during a trainee practitioners learning cycle. The procedure of arterial catheterisation has been broken down into a number of subtasks, one of which requires an operator to locate the femoral artery pulse by palpation. This is performed in preparation for a needle insertion to allow the entry of a guide wire and catheter into the patient. This paper presents the current state of research into a unique solution for affordable haptic simulation of pulse palpation in a virtual environment.

Current challenges in biomanipulations for life-sciences research include extensive operator training, low success rates and low consistency of operations. These problems were tackled here through the use of teleoperation techniques and the development of a unified interface for simultaneous control of all devices used for standard biomanipulations. The developed system was created with high-end commercial biomanipulation equipment similar to those currently in use at many research laboratories, and also included game joysticks for teleoperated control. These were integrated into a single system through the design of modular component abstractions and the implementation of a central control structure. This structure enabled the creation of an open, flexible and user-friendly biomanipulation system for improved operation performance. This paper describes the design and implementation of such system.

As the speed of industrial pick and place robots continues to increase, new trajectory planning strategies will need to be developed in order to optimise the dynamic characteristics of high speed motion. The use of elliptical pick and place cycles coupled with a Modified Sine cam motion profile is proposed and compared to the traditional rectangular cycle with trapezoidal velocity profile. The elliptical cycle exhibits very smooth, continuous motion curves. Asymmetric acceleration can optionally be specified to reduce peak joint torques and increase maximum pick and place speed.

This paper presents the mechanical design of an ankle rehabilitation robotic device based on a 2-dof, redundantly actuated parallel mechanism. The parallel mechanism introduced in this paper has the advantage of mechanical and kinematic simplicity when compared to existing platforms while at the same time it is fully capable of carrying out all the exercises required by ankle rehabilitation protocols. The proposed device makes use of actuation redundancy to eliminate singularity and greatly improve the workspace dexterity. In addition, the requirements for high torque capacity and back-drivability are satisfied with the employment of a custom made cable driven linear electric actuator that combines the high force capacity with excellent back-drivability. The analysis undergoes the optimal design towards the maximization of manipulator workspace, dexterity, torque output and compactness of the device. Finally, the performance of the custom linear actuator and the prototype of the rehabilitation device are shown.

This paper presents the development of a new compact soft actuation unit intended to be used in multi degree of freedom and small scale robotic systems such as the child humanoid robot “iCub” [1]. Compared to the other existing series elastic linear or rotary implementations the proposed design shows high integration density and wider passive deflection. The miniaturization of the newly developed high performance unit was achieved with a use of a new rotary spring module based on a novel arrangement of linear springs.

The MACCEPA (Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator) is an electric actuator of which the compliance and equilibrium position are fully independently controllable and both are set by a dedicated servomotor. In this paper an improvement of the actuator is proposed where the torque-angle curve and consequently the stiffness-angle curve can be modified by choosing an appropriate shape of a profile disk, which replaces the lever arm of the former design. The actuator has a large joint angle, torque and stiffness range and these properties can be made beneficial for safe human robot interaction and the construction of energy efficient walking, hopping and running robots. The ability to store and release energy is shown by simulations on a 1DOF hopping robot. Its hopping height is much higher compared to a configuration in which the same motor is used in a traditional stiff setup. The stiffness of the actuator has a stiffening characteristic so the leg stiffness resembles more a linear stiffness as found in humans.

This paper presents an energy-based control strategy to be used in robotic systems working closely or cooperating with humans. The presented method bounds the dangerous behavior of the robot during the first instants of the impact by limiting the energy stored into the system to a maximum imposed value.Two critical physical human robot interaction (pHRI) cases are studied, these are the collision either against a free or a clamped head. Safe energy values that can be used as reference were retrieved by analysing experimental data of energy absorption to failure of cranium bones and cervical spinal cords.The energy regulation control is implemented in a series elastic actuator prototype joint. The model and the control scheme of the system are analysed. The proposed control scheme is a position-based controller that adjusts the position trajectory reference in function of the maximum energy value imposed by the user. Preliminary results are presented to show that the actuator unit and this control scheme are capable of limiting the energy to a maximum imposed value.

Recent investigations show that compliant systems can be more safe and energy-efficient than conventional stiff actuated systems. As a result, researchers are increasingly implementing compliance within actuation systems using a variety of mechanisms. In general, these actuators can be grouped in 2 main categories. The first category includes all the actuation systems with a compliant element connected in series (SEA), while the second group contains all those systems that employ two actuators placed antagonistically. In both designs the ability to regulate the stiffness is essential in order to meet safety and/or performance demands. Energy consumption is a very important aspect to be considered, especially in autonomous robots. This paper presents a theoretical study on the energy consumption of variable stiffness actuators, comparing the amount of energy required in order to perform a certain task.

The ¿iCub¿ is a robotic platform that was developed within the RobotCub European project to provide the cognition research community with an open ¿child-like¿ humanoid platform for understanding and development of cognitive systems. In this paper we present the mechanical realization of the new lower body developed for the ¿iCub¿ child humanoid robot in order to keep up with the latest technology and solve mechatronic problems found in the previous version. The new lower body assembly demonstrates significant improvements over the old prototype including higher modularity, full joint state sensing and improved range of motion and torque capabilities. In particular the new leg and waist mechanisms to match the size and physical abilities of a 3¿ year old human child are introduced.

The performance of highly dynamic robotic machines is directly associated with both the actuation means and the specific mechanical properties/configuration of the system. Hydraulic actuation demonstrates significant competitive advantages when minimum weight and volume, large forces and wide range of speeds are required and this makes it very suitable for systems such as legged robots. The geometry and design of leg mechanisms have great effect on the actuation system performance such as the required flow, which directly determines the size/weight and power density, in turn affecting the performance of the robot. This paper describes the mechanism and operation principle of two 2-DOF legs considered for HyQ, a hydraulically actuated quadruped robot. Numerical studies have been done to investigate the required flow, the pressure in the actuator chambers and the efficiency of the two leg mechanisms. The results show that the second leg design reduces the required flow significantly with less pressure-jump in the actuator and higher efficiency.

This paper examines the application of a force feedback interface to minimize the effect of the pathological absence of control on the upper limb motion of impaired users. The haptic device used in this research is a two degree of freedom (DOF) Pantograph planar device. Force sensing is employed to detect the user indentation of motion while assistive impedance based techniques were used to develop a clumsy motion suppression control system. The erratic motion suppression techniques and the experimental system setup were evaluated in two dimensional tracking tasks using a human subject with failure of the gross coordination of the upper limb muscle movements resulted by a disorder named dasiaMuscle Ataxiapsila. The preliminary results obtained from these experiments depict that the proposed system can be used to enhance the motion coordination for muscle ataxia impaired subjects in two dimensional tracking tasks, similar to computer mouse interactions.

This paper describes the design, construction and testing of an automated system for the assembly and packaging of triangular sandwiches. This process is currently highly labour intensive with little automated machinery available. This paper analyses the current manual production techniques and develops a number of modular workstations which can be incorporated into an existing line in place of human operators. The machine developed completes the final assembly of the sandwich and then cuts and packages it into a plastic skillet (container) for dispatch. To test the overall performance of the system real plant trials were conducted with the machine in a sandwich production factory and the results of these trials are reported here

The manipulation of flexible and limp sheet materials is a common requirement in many industrial manufacturing processes, however automation of even simple tasks involving these difficult to handle materials tends to be particularly problematic. The industrial manufacture of lasagna ready meals is one example of a process that has been almost completely automated except for the handling of the flexible lasagna pasta, which remains a highly repetitive and labour intensive manual task. In this paper a robotic end-effector is developed to enable the automatic handling of lasagna pasta sheets. The design of the end-effector is described and its performance evaluated through testing using a robot arm. The concept was further developed into a low cost fixed automation machine suitable for industrial use. The pneumatic pick and place type machine was able to pick a pasta sheet from a moving conveyor and place it into a plastic tray with a cycle time of less than 4 seconds.

Nowadays, the understanding of the human cognition and it application to robotic systems forms a great challenge of research. The iCub is a robotic platform that was developed within the RobotCub European project to provide the cognition research community with an open baby- humanoid platform for understanding and development of cognitive systems. In this paper we present the design requirements and mechanical realization of the lower body developed for the "iCub". In particular the leg and the waist mechanisms adopted for lower body to match the size and physical abilities of a 2 frac12 year old human baby are introduced.

This paper reports on the development of two number of robotic hands have been developed which focus on tele-operated high speed anthropomorphic dextrous robotic hands. The aim of developing these hands was to achieve a system that seamlessly interfaced between humans and robots. To provide sensory feedback, to a remote operator tactile sensors were developed to be mounted on the robotic hands. Two systems were developed, the first, being a skin sensor capable of shape reconstruction placed on the palm of the hand to feed back the shape of objects grasped and the second is a highly sensitive tactile array for surface texture identification

The development of successful tactile displays can be assisted by the capacity to assess their performance in integrated virtual reality applications. The design of single tactor or desktop tactile displays has been useful for scientific analysis of the mechanisms of touch. Nevertheless, it does not provide useful information about the quality of immersion in VR environments integrated with tactile feedback where user mobility and comfort are paramount. The present work introduces a new wireless portable tactile display for the finger. This new device demonstrates excellent levels of force output combined with good bandwidth in a configuration of a wearable 4times4 array of vertically moving tactors. The focus of this design is small size, comfort, mobility and ultimately integration into a complete Virtual Reality system for tactile surface texture exploration with 6DOF in the finger's work envelope

This work discusses the design and control approach of a 3 DOF waist joint for the "iCub" humanoid robot. "iCub" is a child like crawling robot that resembles a 2 and a half year old child. The ultimate goal of this project is to provide the cognition research community with an open human like platform for understanding of cognitive systems through the study of cognitive development. The designs of the mechanisms adopted for the waist joint are discussed. This is accompanied by discussion of the control scheme design and presentation of experimental results showing the performance of the mechanism

Hand therapy is a major sector of physiotherapy and one of great importance. The impairment of the hand and generally of the upper limbs can be the cause of social and financial hardship and a serious cause of physical and emotional deterioration. Major efforts are directed into developing therapy methods and procedures in order to standardise and therefore successfully apply treatment regimes in a wide scale. Although, the lack of scientific measurements of statistical value that the current methods suffer due to the mostly empirical nature of examination, assessment and treatment does not assist this endeavour. This paper presents an exoskeleton based system for the physical and occupational therapy of the hand in an interactive VR environment. This system enhances the existing therapy methods with the introduction of accurate and repeatable finger motion and force measurement, interactivity, potential for great exercise assortment and statistical registration and evaluation.

Pneumatic muscle actuators based on McKibben muscles have performance characteristics that may be of considerable significance in robotics due to their power/weight ratio and use as user friendly soft drives. However, the dynamic response (bandwidth) has been inferior to electric systems, with a secondary concern over system stiffness. In this paper, the bandwidth limit is addressed from two perspectives; air flow effects and the physical structure of the actuator. It is shown that by reducing the dead volume within the muscle structure (by the addition of a variety of filler materials) the bandwidth can be increased by up to 400%, with similar increases in system stiffness. At the same time the air volume used to power the actuator can be reduced by up to 80-90%. The methods of achieving these improvements are fully assessed. Also, by ensuring effective air flow rates, it is shown that bandwidth limits can be increased by several 100% and potentially increases of 1000s% are possible.

Traditional robotic/mechatronic design has successfully exploited the attributes of heavy mechanical systems engineering, but future scientific trends suggest a need for technology that will emulate natural systems. Among the most pressing of the requirements are actuation systems that can interact in a safer and more natural way. Pneumatic technology has many of the compliance forms needed for this softer interaction and a number of new systems based on McKibben muscles have been developed in recent years. In this paper a new model of operation of pneumatic muscle systems is developed. In particular, the model considers the distortion effects at the termination nodes and the radial pressure loss due to rubber elasticity. The new model is compared experimentation on a very large actuator and shows how this new model improves the assessment of forces and displacement that can be achieved by the actuator. The new model is compared against previous systems models.

VR and telepresence applications have placed increasing demands on the need for effective user interfaces. To date most of the interfaces have emphasised the use of visual and audio effects but tactile feedback has been identified as a leading feature for future systems where there will be an increased desire to truly interact with the virtual/remote world rather than being observational. The paper focuses on the cutaneous aspects of tactile feedback describing the design and construction of pneumatically powered tactile and shear feedback modules. It is shown that by incorporating a range of novel features into this design it is possible to stimulate all the mechano-receptive nerves (SAI, SAII, RAI, and RAII) with localised signals from DC to 400 Hz. All this is shown in a fully integrated, ultra-light and comfortable package. The design control and performance results are all presented.

The processes of nuclear clean-up, dismantling and decontamination are highly hazardous, but in many instances, particularly where the facility is older there has been little provision for automation, and human intervention is necessary. The paper describes a prototype design of a teleoperational rig for retrieval of radioactive material (spent Magnox fuel) from underwater storage ponds. The system uses a combination of the traditional man-handled manipulation pole combined with new pneumatic muscle actuators. The paper considers the design requirement, the technology and the system performance in dry test operations.

The capability of visualising and touching models of new products or new environments is a key factor in the design to production process. Usually this requires the construction of a physical model of the environment but production of the model can be costly and time consuming. Use of CAD packages can assist with the visualisation process, but proprioceptive and tactile sensations are required to augment this and provide the opportunity to feel the object. This work shows design, construction and testing in a virtual world of a generic 18 DOF proprioceptive input and feedback exoskeleton to monitor the motions of the human arm from sternum/spine to wrist and feedback tactile sensation generated during contact within a virtual world. The design is light, comfortable, and easy to wear for long periods providing an almost complete, unhampered range of input options. The proprioceptive inputs are augmented by tactile feedback of contact pressure (8 sensation points) to the upper and lower arm segments and pressure, texture, slip, edges/ridges/corners and thermal parameters to the hand. The paper shows how the input/feedback exoskeleton can be used to explore CAD designs generated in a commercial package (AutoCad) and imparted directly into a virtual world (WorldToolKit) permitting testing of products/processes before production and thereby improving the design-production process through the enhanced used of concurrent engineering techniques.

This work reports on the construction of components for a humanoid robot powered by a new low mass, high power weight and volume actuation system, called the pneumatic muscle actuator (PMA). In addition to their power and force capabilities the PMA, being pneumatic, produces a more natural human muscle like contact and as such can be considered a soft actuation system with the inherent safety implication when working in close proximity to humans. The integration and testing of the performance of the component sections is also considered to show how these structures and actuators can be combined to produce the various systems needed for a low mass humanoid and the potential for future application in humanoid and other robotic fields.

Bipedal locomotion and particularly the human gait is a highly automated and complex process involving large numbers of actuators. Unfortunately actuator technology is an area of robotics with many conflicting requirements such as: high power density, high power to weight ratio, rapid response, accurate repeatable control, cleanliness, high efficiency, and low cost, which make selection complex. Pneumatic muscle actuators (PMA) [based on the McKibben Muscle] which can provide position and force control better than 1% have been applied to upper limbs with some success and with contractile forces in excess of 1000N (in units weighing less than 50g) there is considerable potential for use in bipedal locomotion. This paper will explore the design of a bipedal robot to take advantage of the potential of these actuators. Muscle co-ordination and control sequences will be considered for striding and standing activities and it will be shown that in terms of the energy requirements PMAs are very capable of providing a reliable bipedal drive source with linear actuation, low mass, fast response, compliant energy storage and simple construction.

Haptic sensation has two complex components; skin (cutaneous) sensing which is mediated by a variety of sensing organs that respond to pressure, vibration, displacement and temperature and kinaesthetic/proprioceptive sensing (muscles and joints) which responds to motions and forces exerted by the interaction of the body with the external environment. Although haptic interaction has been identified as being crucial for many applications, achieving realism in haptic feedback has not been possible due to physical, understanding and modelling problems. This paper explores the sensation of touch from a physiological and technological perspective and shows how this can be combined with an integrated touch/force reflecting system to produce a 'realistic' haptic rendering.

Applications in telepresence, virtual reality and virtual environments have highlighted the need for an advanced realistic user interface to remote and virtual entities. Vision and audition have been well developed leaving touch as a key sensory parameter which is presently under-utilised. In this paper a detailed study of the present knowledge of the human sensory nervous system relating to the 'sense of touch and feeling' is undertaken. Using this knowledge of the operation of tactile nerves a multi-modal cutaneous tactile feedback glove has been designed and tested. The construction of this system which feeds back data on texture/slip motion, edges/ridges and surface contours, contact force and thermal parameters is detailed with the test results and system assessment.

Robots have most successfully been applied in repetitive operations, but often when the task involves complex variable operations in unstructured environments teleoperation is preferred. As the complexity of these human supervisory tasks has increased the trend has been towards greater sensory feedback and more intuitive input control. This paper reports on the relative effectiveness of and need for sensory feedback systems for operator control in telepresence applications. In particular, studies were made of the performance of manipulation and navigation planning operations on a twin armed mobile robot using a variety of visual and audio cues and input systems. The performance was measured by experimentation with a range of subjects at a number of difficulty levels to test the effectiveness of the telepresence controller in a series of technical scenarios.

Haptic cues are the forms of information that can be acquired by the human sensory system through touching or handling an object. Some cues require active exploration while others are passively received. A basic taxonomy of haptic cues and the role that each cue plays in grasping and manipulation is illustrated in this paper. An initial analysis considers all forms of sensing and feedback (visual, audio, smell and taste) but attention is focused on global tactile requirements (kinaesthetic) and the local tactile (cutaneous) sensory cues. A multi-modal feedback system is shown which can stimulate a range of tactile nerves (mid/mid and low frequency plus thermal), together with the test results. Particular attention is applied to the use of mid-high frequency stimulation, where interesting sensations have been recorded which have a direct bearing on the ability of a user to use this form of system for prolonged periods.

Developments in telepresence have served to demonstrate the requirements for an advanced realistic user interface to remote and virtual entities. Most of these applications have concentrated on visual and audio feedback but recent research has highlighted the need for tactile input, control and feedback. This paper reports on the operator input/feedback robot sensory/control aspects of a dexterous manipulation system possessed of two arms (left and right) and two dexterous multi-jointed skeletal 'hands'. The operator input units developed include a light weight 7 degree of freedom arm exoskeleton combined with a cutaneous tactile feedback glove providing finger input control and tactile feedback of contact force, vibrational and thermal data in a system with an overall mass of less than 0.75 kg. This provides a user friendly system that can be used for extended periods without excessive strain.

Problems with the control and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio and inherent safety are factors that could potentially be exploited in sophisticated dexterous manipulator designs. These advantages have lead to the development of novel actuators such as the McKibben Muscle, Rubbertuator, Flexator, Romac and pneumatic muscle actuators (PMA). However, the nonlinearities in these systems still limit their controllability. This paper considers the development of an adaptive controller for bi-muscular PMA system. Control of these muscles is explored via adaptive pole-placement controllers. Experimental results indicate that accurate position control /spl plusmn/2/spl deg/ is feasible, with power/weight outputs in excess of 1 kW/kg at 250 kPa being possible.

Problems with the control and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio and inherent safety are factors that could potentially be exploited in sophisticated dexterous manipulator designs. This paper considers the development of a new high power/weight and power/volume braided pneumatic muscle actuator (PMA) having considerable power output potential, combined with controllable motion and inherent compliance to prevent damage to handled objects. Control of these muscles is explored via adaptive pole-placement controllers. Experimental results indicate that accurate position control /spl plusmn/1/spl deg/ is feasible with power/weight outputs in excess of 1 kW/kg at 200 kPa being possible.>

Robots are often required to function in environments which would be extremely dangerous or expensive when using direct human labour, however, computer control and intelligence are not sufficiently developed to permit the robots to perform these advanced technical tasks under their own initiative, and there is always a human operative in the loop. Ideally the operator would wish to input body motions (from legs, arm, hand and head) which the robot would duplicate, and receive from the remote sensors full visual, audio and tactile feedback of a quality and form comparable with that normally produced by the eyes, ears and skin. This work considers the development of input, control and feedback (visual, audio and tactile) systems (man-machine interface) for a twin armed mobile robot to be used in tele-presence applications. This multi-purpose human-machine interface provides the user with an enhanced degree of true control of and 'feel' for the task.>

Hydraulic, pneumatic and electrical systems form the principle power sources that have been used to operate robots in industrial environments. However, when considering the needs of advanced robotics, one of the most fundamental requirements and problems is the provision of a suitable energy source to power the robot in a nonindustrial, often remote site. This work is aimed at designing a portable power supply that will operate efficiently and effectively in an untethered environment using a chemical fuel. Initially a feasibility study was undertaken to identify the different energy sources that are most widely used in the field of industrial robots, noting their relative advantages and disadvantages. Subsequently, the report focuses on the design, implementation and testing of an internal combustion based system that will be used to provide the drive source to a compressor which in turn will produce a constant air supply via a clutch and a series of gear mechanisms. Development and application of the mobile power supply is detailed. The silencing of the system is also analysed to reduce the noise level and allow operation in an enclosed/restricted environment as well as in the open atmosphere. This chemo-pneumatic power source is subsequently shown in operation driving pneumatic muscle actuators used in the construction of a high power/weight dexterous (3 finger 1 thumb) hand.>

Robots are often required to function in areas that are too dangerous or expensive for direct human labour. Unfortunately the unstructured nature of many of these environments and complexity of the task make autonomous operation impossible and tele-operation is essential. A variety of tele-operated devices are available to assist the operator, but in general control of the actions is not truly intuitive. Ideally the operator would wish to input body motions (from legs, arm, hand and head) which the robot would duplicate, and receive from the remote sensors, feedback of a quality and form comparable with that normally sensed. This work considers the development and testing of enhanced input, control and feedback (visual audio and tactile) systems for a twin armed mobile robot to be used in tele-presence applications. In particular the authors focus on the performance of the operator in controlled motion and manipulation tasks. These tests involve assessment of the usefulness of tactile, video and audio feedback parameters including: wide/narrow angle field of view, colour/monochrome, stereo/mono vision and audio. The performance is assessed in terms of the ease of training, time to perform simple functions and accuracy of task completion.>

Describes the development of a multi-sensor tactile digit. This instrumented finger has the ability to detect contact pressure/force, hardness, texture, temperature, slip, surface profile/shape, and thermal conductivity. This information is subsequently transferred directly from the robot to the skin of the remote operator using tactile feedback units based on piezo, vibro and thermal effects. This multipurpose human-machine interface (tele-taction) provides the user with an enhanced degree of true feel for a grasped object.

The varied range of components that must be detected makes tactile Sensors the most sophisticated set of sensing organs in the human body. Unfortunately this complexity also means that there is no effective robotic equivalent to the nerves in skin. This paper reports on the development of a multi-functional tactile sensor capable of collecting data on an broad range of tactile parameters such as contact pressurefforce, texture, hardness, shear, surface profile and slip. This extensive range provides an excellent vehicle for sensor fusion development both at the primary detection level and at the higher perceptual level where data from the sensory sets are fused by an expert identification system. This information has been combined to form an expert identifier for object classification (metal, glass, stone, wood, plastic, paper, and fabric) using only touch.