Wearable assistive devices that monitor muscle fatigue reduce the risk of work-related musculoskeletal disorders, enhance rehabilitation outcomes, and extend operational time by optimizing the power consumption of the device. This work proposes a muscle fatigue-aware controller (MFAC) for a semi-rigid knee exoskeleton. During an offline calibration phase, we use Gaussian Process Regression (GPR) to model the relationship between muscle activation (measured via EMG) and the corresponding joint moment and angle, enabling fatigue state estimation for the controller. The trained model then approximates muscle activation online using only joint states and moment derived from user’s kinematic data and ground reaction forces provided by the wearable device. The estimated muscle activation is used to assess the muscle fatigue state through a model-based fatigue evaluation module. Notably, EMG measurement is only required during the offline training in our approach, enabling EMG-free online estimation, which significantly enhances the feasibility for long-term mobile applications. Building on muscle fatigue and human-exoskeleton interaction models, we then developed an adaptive controller within a predictive control framework. The resulting optimization problem generates control signals that adjust assistance to reduce the fatigue progression. Two experiments validate the EMG-free fatigue estimation method and the integrated MFAC, demonstrating accurate muscle activation estimation and effective adaptive assistance based on the estimated fatigue state. Analysis of actuator power output reveals adaptivity in which the controller conserves energy during low muscle fatigue and progressively improves power output with increasing fatigue, suggesting a longer duration of the device with a fixed battery capacity.

Non-prehensile pushing to move and reorient objects to a goal is a versatile loco-manipulation skill. In the real world, the object's physical properties and friction with the floor contain significant uncertainties, which makes the task challenging for a mobile manipulator. In this paper, we develop a learning-based controller for a mobile manipulator to move an unknown object to a desired position and yaw orientation through a sequence of pushing actions. The proposed controller for the robotic arm and the mobile base motion is trained using a constrained Reinforcement Learning (RL) formulation. We demonstrate its capability in experiments with a quadrupedal robot equipped with an arm. The learned policy achieves a success rate of 91.35% in simulation and at least 80% on hardware in challenging scenarios. Through our extensive hardware experiments, we show that the approach demonstrates high robustness against unknown objects of different masses, materials, sizes, and shapes. It reactively discovers the pushing location and direction, thus achieving contact-rich behavior while observing only the pose of the object. Additionally, we demonstrate the adaptive behavior of the learned policy towards preventing the object from toppling.

In scenarios of Human-Robot Interaction (HRI), it is often assumed that the robot should cooperate with the closest individual or that only one person is present. However, in real-life situations, such as shop floor operations, this assumption may not hold. Thus, it becomes necessary for a robot to recognize a specific target in a crowded environment. To address this problem, we propose a person re-identification module that uses continuous visual adaptation techniques. This module ensures that the robot can seamlessly cooperate with the appropriate individual despite its appearance changes or partial or total occlusions. We used both a laboratory environment and an HRI scenario where the robot followed a person to test our framework. During the test, the targets were asked to change their appearance and disappear from the camera’s field of view to test the module’s ability to handle challenging cases of occlusion and outfit variations. We compared our framework with a state-of-the-art Multi-Object Tracking (MOT) method, and the results showed that our module, shortly named CARPE-ID, accurately tracked each selected target throughout the experiments in all cases except for two cases. In contrast, the MOT had an average of 4 tracking errors for each video.

Hand motion tracking is essential in many fields, e.g., immersive virtual reality, teleoperation of robotic hand, and hand rehabilitation of stroke patient, as human hand plays a crucial role in our daily life. The highly under-actuated hand exoskeleton, which can track the 6-DoF motions of each fingertip via a highly under-actuated kinematic chain, exhibits many benefits in wearability and portability over other solutions. However, due to the non-anthropomorphic linkage, this hand exoskeleton also encounters difficulties in measuring human-finger’s joint angles. While the joint-space is important in many scenarios, such as teleoperating a robotic hand with anthropomorphic kinematics but with different size to human. Here we proposed a new method to reconstruct the human finger joints by using a highly under-actuated hand exoskeleton. Our key contribution is the arc-fitting algorithm, which is able to calibrate the misalignment between the exoskeleton’s and the human-finger’s base frames and estimate the length of human’s phalanxes, by using the fingertip’s circular motions. With knowing the aforementioned informations, the joint angles can be reconstructed in high precision based on the inverse kinematics models of human fingers. Furthermore, our proposed method is compared with a baseline method, in which the joint angles obtained by a motion capture system are served as ground-truth. The results demonstrate that our proposed method exhibits excellent performance in reconstructing finger’s joint configurations.

The teleoperation of complex, kinematically redundant robots with loco-manipulation capabilities represents a challenge for human operators, who have to learn how to operate the many degrees of freedom of the robot to accomplish a desired task. In this context, developing an easy-to-learn and easy-to-use human-robot interface is paramount. Recent works introduced a novel teleoperation concept, which relies on a virtual physical interaction interface between the human operator and the remote robot equivalent to a "Marionette" control, but whose feedback was limited to only visual feedback on the human side. In this paper, we propose extending the "Marionette" interface by adding a wearable haptic interface to cope with the limitations given by the previous works. Leveraging the additional haptic feedback modality, the human operator gains full sensorimotor control over the robot, and the awareness about the robot’s response and interactions with the environment is greatly improved. We evaluated the proposed interface and the related teleoperation framework with naive users, assessing the teleoperation performance and the user experience with and without haptic feedback. The conducted experiments consisted in a loco-manipulation mission with the CENTAURO robot, a hybrid leg-wheel quadruped with a humanoid dual-arm upper body.

Modular reconfigurable manipulators enable quick adaptation and versatility to address different application environments and tailor to the specific requirements of the tasks. Task performance significantly depends on the manipulator’s mounted pose and morphology design, therefore posing the need of methodologies for selecting suitable modular robot configurations and mounted pose that can address the specific task requirements and required performance. Morphological changes in modular robots can be derived through a discrete optimization process that involves the selective addition or removal of modules. In contrast, the adjustment of the mounted pose operates within a continuous space, allowing for smooth and precise alterations in both orientation and position. This work introduces a computational framework that simultaneously optimizes the pose and morphology mounted on modular manipulators. The core of the work is that we design a mapping function that implicitly captures the morphological state of manipulators in the continuous space. This transformation function unifies the optimization of mounted pose and morphology within a continuous space. Furthermore, our optimization framework incorporates a array of performance metrics, such as minimum joint effort and maximum manipulability, and considerations for trajectory execution error and physical and collision constraints. To highlight our method’s benefits, we compare it with previous methods that framed such problems as a combinatorial optimization problem and demonstrate its practicality in selecting the modular robot configuration for executing a drilling task with the CONCERT modular robotic platform.

Enabling humanoid robots to perform autonomously loco-manipulation in unstructured environments is crucial and highly challenging for achieving embodied intelligence. This involves robots being able to plan their actions and behaviors in long-horizon tasks while using multi-modality to perceive deviations between task execution and high-level planning. Recently, large language models (LLMs) have demonstrated powerful planning and reasoning capabilities for comprehension and processing of semantic information through robot control tasks, as well as the usability of analytical judgment and decision-making for multi-modal inputs. To leverage the power of LLMs towards humanoid loco-manipulation, we propose a novel language-model based framework that enables robots to autonomously plan behaviors and low-level execution under given textual instructions, while observing and correcting failures that may occur during task execution. To systematically evaluate this framework in grounding LLMs, we created the robot ’action’ and ’sensing’ behavior library for task planning, and conducted mobile manipulation tasks and experiments in both simulated and real environments using the CENTAURO robot, and verified the effectiveness and application of this approach in robotic tasks with autonomous behavioral planning. Video: https://youtu.be/mmnaxthEX34

This work presents the computational design and validation of the Multi-Arm Relocatable Manipulator (MARM), a three-limb robot for space applications, with particular reference to the MIRROR (i.e., the Multi-arm Installation Robot for Readying ORUs and Reflectors) use-case scenario as proposed by the European Space Agency. A holistic computational design and validation pipeline is proposed, with the aim of comparing different limb designs, as well as ensuring that valid limb candidates enable MARM to perform the complex loco-manipulation tasks required. Moti-vated by the task complexity in terms of kinematic reachability, (self)-collision avoidance, contact wrench limits, and motor torque limits affecting Earth experiments, this work leverages on multiple state-of-art planning and control approaches to aid the robot design and validation. These include sampling-based planning on manifolds, non-linear trajectory optimization, and quadratic programs for inverse dynamics computations with constraints. Finally, we present the attained MARM design and conduct preliminary tests for hardware validation through a set of lab experiments.

The Incomplete Orientation Constraint (IOC), which does not require a controlled motion constrained by all three spatial directions, exists widely in a lot of robotic tasks. However, the IOC remains a challenge for existing methods, due to the nonlinear structure of the rotation group SO(3). Moreover, the IOCs are time varying in the trajectory tracking problem, which makes it more challenging than the set-point control. To address the IOC problems, we define, identify and prove the closed-form solution of the perpendicular curve in SO(3). Based on the proposed perpendicular curve, we develop a new trajectory tracking controller considering the IOC. Compared with existing methods, the proposed method can achieve faster and more accurate tracking results. Moreover, the proposed method can be applied not only to manipulators with redundancy (including both functional and intrinsic redundancy), but also to manipulators that are non-redundant. Furthermore, it is easier to incorporate a secondary optimization objective into consideration for the intrinsic redundant case, which is difficult for existing methods. The proposed method has been implemented on both simulations and experiments. The numerous simulation and experimental results validate the effectiveness and advantages of the proposed method. Note to Practitioners—The trajectory tracking control with IOC is important and can be applied in a wide spectrum of robotic-assisted manufacturing, e.g. arc-welding, engraving, etc.. In this paper, a perpendicular curve-based trajectory tracking method is proposed to consider the IOC automatically. It is no longer necessary to carefully plan a reachable orientation trajectory. Users only need to focus the planning of the tool direction, which is determined by the tasks. In addition, the proposed method can achieve faster and more accurate tracking result. Moreover, it is universal and can be applied to both redundant and non-redundant cases.

Wheeled-legged robots have the potential to navigate in cluttered and irregular scenarios by altering the locomotion modes to adapt to the terrain challenges and effectively reach targeted locations in unstructured spaces. To achieve this functionality, a hybrid locomotion planner is necessary. In this work we present a search-based planner, which explores a set of motion primitives and a 2.5D traversability map extracted from the environment to generate navigation plans for the hybrid mobility robot CENTAURO. The planner explores the map from the current robot position to the goal location requested by the user, considering the most appropriate composition and tuning of locomotion primitives to build up a feasible plan, which is then executed by the robot. The available primitives are prioritized and can be easily modified, added or removed through a configuration file. Our approach was evaluated both in simulation and on the real wheeled-legged robot CENTAURO, demonstrating traversing capabilities in cluttered environments with various obstacles.

The teleoperation of mobile manipulators may pose significant challenges, demanding complex interfaces and causing a substantial burden to the human operator due to the need to switch continuously from the manipulation of the arm to the control of the mobile platform. Hence, several works have considered to exploit shared control techniques to overcome this issue and, in general, to facilitate the task execution. This work proposes a manipulability-aware shared locoma-nipulation motion generation method to facilitate the execution of telemanipulation tasks with mobile manipulators. The method uses the manipulability level of the end-effector to control the generation of the mobile base and manipulator motions, facilitating their simultaneous control by the operator while executing telemanipulation tasks. Therefore, the operator can exclusively control the end -effector, while the underlying ar-chitecture generates the mobile platform commands depending on the end-effector manipulability level. The effectiveness of this approach is demonstrated with a number of experiments in which the CENTAURO robot, a hybrid leg-wheel platform with an anthropomorphic upper body, is teleoperated to execute a set of telemanipulation tasks.

Envisioning the employment of rope-assisted humanoid robots to reduce human intervention for operations in the heights, this preliminary work addresses the modeling and motion planning problems for a rope-assisted bipedal robot. The mathematical features of this system outnumber the ones of typical humanoid robots, including: under-actuation of the floating-base joints, the rope pulling effect and the passive connection between the robot body and the rope master-point. These characteristics render the study of a rope-assisted bipedal robot both fascinating and unexplored, raising motion planning challenges when attempting to plan dynamic suspended maneuvers, as rappelling. To this end, we first introduce a template three-mass model of a bipedal robot connected through passive joints to an extensible rope, which is in turn modeled as a two-mass body. Based on this, a family of optimal control problems is presented to plan rappelling maneuvers.

The kinematic features of a centaur-type humanoid platform, combined with a powerful actuation, enable the experimentation of a variety of agile and dynamic motions. However, the higher number of degrees-of-freedom and the increased weight of the system, compared to the bipedal and quadrupedal counterparts, pose significant research challenges in terms of computational load and real implementation. To this end, this work presents a control architecture to perform agile actions, conceived for torque-controlled platforms, which decouples for computational purposes offline optimal control planning of lower-body primitives, based on a template kinematic model, and online control of the upper-body motion to maintain balance. Three stabilizing strategies are presented, whose performance is compared in two types of simulated jumps, while experimental validation is performed on a half-squat jump using the CENTAURO robot.

The flexibility and adaptability of modular and re-configurable robots opens up new opportunities for on-demand robot morphology optimization for varying tasks. In particular, multi-arm robotic systems can expand the solution space for any given task. In this paper, we present a novel approach to exploit this feature for generating optimal fit-to-task robot structures with respect to a minimum-effort objective. By describing the task in terms of relative poses between the end-effector and the constraint frame, and making use of the relative Jacobian, the minimum effort optimization problem can be equally expressed for single-arm or multi-arm robots. We test our approach for a peg-in-hole and a contour-following task and compare the performance of the optimal solution obtained with that of a standard manipulator configuration.

This paper presents a reactive legged locomotion generation scheme that enables our quadruped robot CEN-TAURO to adapt to varying payloads while walking. The center-of-mass (CoM) trajectories are generated in real time in a model predictive control (MPC) fashion, trading off large stability margins against evenly stretched legs. Vertex-based zero-moment-point (ZMP) constraints are imposed to ensure quasi-static walking stability. A Kalman filter is then implemented to estimate the CoM states and the impact of external payloads which can vary online and affect/disturb the locomotion differently. The CoM estimation is used to update the MPC motion planner at every replanning instant so that the robot can react to unknown and time-varying payloads on the fly.We validate the proposed scheme through experimental trials where the robot walks on flat ground or steps on different surface levels while carrying heavy payloads. It is shown that the proposed reactive locomotion strategy enables the robot to carry 20 kg payloads, which is close to the maximum capacity of the robot arms.

Kinematic and force synergies can be used to reduce the complexity and dimensionality of the motion generation and control problem, as well as facilitate the mechanical implementation of robotic hands. In this paper we present a novel implementation of hardware synergies realized on the actuation level by leveraging a novel reconfigurable electric actuation topology principle. The proposed electric actuation topology enables different actuation synergies by changing the interconnections among the actuators at the electrical/motor driver level. We describe the synergies and their implementation in a port-based context, and elaborate how equivalent hard and soft synergies emerge from the electric power flow within different actuation topologies. We realize the reconfigurable electric actuation topology scheme in the HERI III hand, a novel robust and powerful robotic gripper, also introduced in this paper, resulting in easy to control behaviours like on industrial grippers or underactuated hands, but with the high grasping versatility of fully actuated hands. Finally we present grasping experiments performed on the HERI III hand that clearly show the desired behaviours and validate the innovative proposed scheme.

The use of assistive technologies in industrial environments to improve human ergonomics and comfort in repetitive and high effort tasks have increased considerably in the last decade. Predominantly, the goal is to provide additional physical support through lightweight and wearable devices, without posing major constraints to the human body movements. Towards achieving this objective, in this work we present a novel actuation mechanism for a soft assistive device, by taking into account the human elbow torque-angle profile. The proposed design integrates a single motor coupled with an elastic bungee and a cam-spool mechanism to enable energy exchange during the elbow flexion movement, while allowing for free-motions during the extension of the joint. A cable-driven transmission with passive elastic attachments is employed to implement compliant couplings with the wearer and to achieve easy donning/doffing. Experiments are conducted on two 3D printed functional prototypes. Results suggest that the assistive elbow torque is effectively transmitted with an average 90% success for balancing a 5N payload, and the free-motion range of 108° is measured for both flexion and extension.

Torque distribution in redundant robots that combine the potential of asymmetric series-parallel actuated branches and multi-articulation pose a non-trivial challenge. To address the problem, this work proposes a novel optimization based controller that can accommodate various quadratic criteria to perform the torque distribution among dissimilar series and parallel actuators in order to maximize the motion efficiency. Three candidate criteria are composed and their performances are compared during periodic squat motions with a 3 degree of freedom series-parallel compliant articulated leg prototype. It is first shown that by minimizing a criterion that takes into account the actuator hardware specifications such as torque constant and transmission ratio, the gravity-driven phases can be lengthened. Thereby, this particular criterion results in slightly better performance than when adopting a strategy that maximizes the torque allocation to the higher efficiency actuators. Furthermore, valuable insights such as that the efficacy of maximum utilization of the highly-efficient parallel actuation branches decreases progressively at high frequencies were observed.

Orientability is an important performance index to evaluate the dexterity of haptic master devices. Currently, most of the existing haptic master devices have limited workspace and limited dexterity. In this paper, we present the RemoT-ARM, a 6 Degree-of-Freedom (DOF) haptic master device that can provide larger and more dexterous workspace for operators. To evaluate its reachability of orientations, we propose a novel orientability index. Furthermore, a relative orientability index is proposed to characterize the matching degree of the workspace of a given manipulator to its target workspace. The volume, the manipulability and the condition number are also introduced as performance indices to evaluate the size and the isotropy of the workspace. According to these performance indices, all possible configurations for the RemoT-ARM have been taken into consideration, analyzed, and compared to finalize its optimal configuration.

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.

Despite plenty of motion planning strategies have been proposed for bipedal locomotion, enhancing the walking robustness in real-world environments is still an open question. This paper focuses on robust body and leg trajectories synthesis through integrating constrained optimization with imitation learning. Specifically, we first propose a Quadratically Constrained Quadratic Programming (QCQP) algorithm to make use of the ankle strategy and stepping strategy. Based on the Linear Inverted Pendulum (LIP) model, body motion can be determined by the modulated Center of Pressure (CoP) position and step parameters (including step location and step duration). After that, we exploit an imitation learning approach Kernelized Movement Primitives (KMP) to plan robot leg motions, which allows for adapting the learned motion patterns to new situations (e.g., passing through various desired points) in a straightforward manner. Several LIP simulations and whole-body dynamic simulations demonstrate that higher walking robustness can be achieved using our framework.

This paper presents a framework providing a full pipeline to execute a complex physical interaction behaviour of a humanoid bipedal robot, both from a theoretical and a practical standpoint. Building from a multi-contact control architecture that combines contact planning and reactive force distribution capabilities, the main contribution of this work consists in the integration of a sample-based motion planning layer conceived for transitioning movements where obstacle and self-collisions avoidance is involved. To plan these motions we use Rapidly Exploring Random Tree (RRT) projected on the contacts manifold and validated through the Centroidal Statics (CS) model, to ensure static balance on non-coplanar surfaces. Finally, we successfully validate the presented planning and control architecture on the humanoid robot COMAN+ performing a wall-plank task.

Nowadays, robots are expected to enter in various application scenarios and interact with unknown and dynamically changing environments. This highlights the need for creating autonomous robot behaviours to explore such environments, identify their characteristics and adapt, and build knowledge for future interactions. To respond to this need, in this paper we present a novel framework that integrates multiple components to achieve a context-aware and adaptive interaction between the robot and uncertain environments. The core of this framework is a novel self-tuning impedance controller that regulates robot quasi-static parameters, i.e., stiffness and damping, based on the robot sensory data and vision. The tuning of the parameters is achieved only in the direction(s) of interaction or movement, by distinguishing expected interactions from external disturbances. A vision module is developed to recognize the environmental characteristics and to associate them to the previously/newly identified interaction parameters, with the robot always being able to adapt to the new changes or unexpected situations. This enables a faster robot adaptability, starting from better initial interaction parameters. The framework is evaluated experimentally in an agricultural task, where the robot effectively interacts with various deformable environments.

When experiencing disturbances during locomotion, human beings use several strategies to maintain balance, e.g. changing posture, modulating step frequency and location. However, when it comes to the gait generation for humanoid robots, modifying step time or body posture in real time introduces nonlinearities in the walking dynamics, thus increases the complexity of the planning. In this paper, we propose a two-layer hierarchical optimization framework to address this issue and provide the humanoids with the abilities of step time and step location adjustment, Center of Mass (CoM) height variation and angular momentum adaptation. In the first layer, times and locations of consecutive two steps are modulated online based on the current CoM state using the Linear Inverted Pendulum Model. By introducing new optimization variables to substitute the hyperbolic functions of step time, the derivatives of the objective function and feasibility constraints are analytically derived, thus reduces the computational cost. Then, taking the generated horizontal CoM trajectory, step times and step locations as inputs, CoM height and angular momentum changes are optimized by the second layer nonlinear model predictive control. This whole procedure will be repeated until the termination condition is met. The improved recovery capability under external disturbances is validated in simulation studies.

Humans commonly exploit interaction with the environment constraints to assist the execution of the loco-manipulation tasks they perform. One particular example is the exploration of contacts during manipulation to relax the loading of those arm joints that are not directly involved in the generation of the manipulation motions and forces, e.g. establishing a contact with the elbow joint to reduce the effort of the upper arm while executing wrist level manipulation. In this paper, we shall explore the possibility of actively (a) utilizing the environment for a non-end-effector support contact towards reducing the joints efforts during manipulation tasks. This is achieved by our proposed control scheme with a three-level hierarchical compliance controller. The highest priority task is assigned to an impedance control that regulates the interaction at the contact control point on the arm in the normal direction of the support plane prior to contact, and is switched to an optimal contact force control for minimizing the joint effort after the contact is built. The second priority task is an impedance control at the same point in the tangential directions of the plane to stabilize the contact. In the end, an impedance behavior at the end-effector is designed to deal with the interaction forces required by the manipulation tasks. The efficacy of the proposed control scheme was corroborated by simulations and experiments, where significant joint effort reduction was observed.

This work introduces a framework for the Cartesian control of multi-legged, highly redundant robots. The proposed framework allows the untrained user to perform complex motion tasks with robotics platforms by leveraging a simple, auto-generated ROS-based interface. Contrary to other motion control frameworks (e.g. ROS MoveIt!), we focus on the execution of Cartesian trajectories that are specified online, rather than planned in advance, as it is the case, for instance, in tele-operation and locomotion tasks. Moreover, we address the problem of generating such motions within a hard real-time (RT) control loop. Finally, we demonstrate the capabilities of our framework both on the COMAN + humanoid robot, and on the hybrid wheeled-legged quadruped CENTAURO.

Linear Series Elastic Actuators exhibit a restricted design space. This inevitably leads to design trade-offs translating into robot performance limitations. These prevent robots from eventually reaching human comparable soft but also powerful physical interaction performance.This work presents a novel fixed passive rolling flexure design principle enabling the realization of a wide range of progressive torque-deflection characteristics. The proposed principle displays low hysteresis and can be manufactured in single 2D components. The paper derives the analytic foundation for the rolling flexure principle and is supported by numerical finite element analysis. The theory is validated by experimental results obtained on two laboratory prototypes.

In this paper, we look at the challenge of human robot interaction in locomotion. We consider a hand-in-hand interaction scenario where a human compliantly interacts with the upper-body of an impedance controlled humanoid. By exploring the velocity transmission of the robot arms, and the interaction in terms of robot arms manipulation quality evaluated through the monitoring of their manipulability the proposed method derives suitable reactive steps in appropriate directions to ensure that the robot manipulation ability is maintained with the robot arms providing high capacity of motion along the different directions. The proposed approach can be combined with different walking pattern generators and is not tailored to a specific one used in this work. The results of the proposed method are experimentally validated on the COMAN + humanoid robot showing the efficacy of the method to generate reactive stepping driven by the interaction and manipulation motion of the human operator. Besides, the work also provides a real-time software architecture to control humanoid COMAN+, but it is also flexible to be used for the control of other robot platforms.

Human Robot interaction is a fundamental perquisite for any robot performing a physical task in collaboration with a human. The presence of disturbances arising from the partially known tasks payloads, the unexpected interaction forces in general, and the uncertainty in the interpretation of the human intention in terms of motions and forces can pose significant challenges and eventually compromise the execution of the collaborative task. This work presents a novel, intrinsically adaptable multimodal (force, motion and verbal) interaction framework for human-robot collaboration (HRC) that leverages on an online self-tuning stiffness regulation principle to provide adaptation to interaction/payload forces and reject disturbances arising by unexpected interaction loads. Besides the presented method, it enables the rejection of unnecessary motion commands (e.g. oscillations generated by the human operator) to reach the robot co-worker through the filtering of the human generated motions, that are outside the range (in terms of speed and acceleration) of the envisioned manipulation manoeuvres. Finally, a verbal interaction channel allows the operator to convey securely his high level intentions and to control the states of the task execution. We evaluated and demonstrated the effectiveness of the proposed multimodal interaction framework in a high weight carrying human-robot collaboration task using the humanoid robot COMAN +.

The deployment of robotics in real-world scenarios, which may involve harsh and irregular physical interactions with the environment, such as those when robots operating in a disaster scenario, or the interactions that prosthetic devices may experience, demands hardware, which is physically resilient. The end-effectors, as the main media of interaction, are probably the parts at the highest risk. The capability of robotic hands to survive severe impacts is thus a necessity for the effective deployment of reliable robotic solutions in real-world tasks. Although, this robustness capability has been noted and discussed in the robotics community for long time, the literature does not provide a systematic study nor there is any proposal of standardized test or metric to evaluate hand resilience. In this work, inspired by the works of Charpy and Izod for the systematic definition of resilience and toughness of materials through impact tests, we consider extending the standard test to robot hands. We introduce a resilience evaluation framework, including a precisely defined experimental set-up and test procedure. As an example of application of the procedure, we apply it to experimentally characterize two robot hands, with a similar conceptual architecture but different size and material. From these tests we obtain several insights, including the observation that the dominant factor in hand resilience is their compliance and actuation principle, and that the use, under certain design conditions, of lightweight materials, such as plastic instead of aluminum, may not necessarily reduce the mechanical strength of the overall system.

As robotic systems become more flexible and intelligent, they must be able to move into environments with a high degree of uncertainty or clutter, such as our homes, workplaces, and the outdoors. In these unstructured scenarios, it is possible that the body of the robot collides with its surroundings. As such, it would be desirable to characterise these contacts in terms of their location and interaction forces. This paper addresses the problem of detecting and isolating collisions between a robotic manipulator and its environment, using only on-board joint torque and position sensing. The algorithm is based on a particle filter and, under some assumptions, is able to identify the contact location anywhere on the robot body. It requires the robot to perform small exploratory movements, progressively integrating the new sensing information through a Bayesian framework. The approach was tested and benchmarked in simulation, with respect to its accuracy and robustness. Validation using a robot with joint torque sensing in a real environment demonstrated the applicability of the method to real-world scenarios.

The modulation of step location and duration plays an important role in realizing robust bipedal walking. This paper formulates it as a nonlinear programming problem (NLP) and proposes a novel optimization approach to adjust step location and duration in real time. Based on state feedback, the Linear Inverted Pendulum dynamics is exploited to determine the optimal step parameters. Different from previous works, this work presents three main characteristics: i) the hyperbolic functions of step duration rather than the step duration itself are chosen to be optimization variables; ii) the approach can be switched from baseline two-steps-prediction optimization to one-step-prediction optimization through merely adding several equality constraints in problem formulation; iii) the approach can deal with relative step location tracking (velocity tracking) or absolute step location tracking (position tracking) via changing the reference step parameters. As a result, the first characteristic enables the NLP to be solved in a computational-efficient manner and the latter two endow the approach with versatility under different control modes. The effectiveness has been demonstrated by simulation experiments.

This paper presents a dynamic whole-body control method for humanoids to render agile and stable standing-up motion based on energy concepts. First, to cope with the standing-up problem with multiple contacts in hierarchical tasks, an enhanced operational-space based whole-body control (WBC) framework is proposed, which offers optimal torque resolutions guaranteeing strict dynamic consistency with inequality constraints formulated by quadratic programming. Second, agile standing-up control strategy with dynamic push and rise actions is newly developed based on the notion of the total energy. The optimal pushing wrenches at contacts are computed to obtain sufficient energy to accelerate the center-of-mass (CoM) of the robot as quick as possible, and the total energy is then controlled to attain rapid rise-up motion and to stabilize the body of the robot. Consequently, the robot can effectively and actively stand up to recover from a certain pose in which cannot be accomplished by any quasi-static motion. The proposed method is numerically experimented and validated with dynamic parameters from the real humanoid COMAN+, fulfilling different types of standing-up actions.

Contemporary humanoids are equipped with visual and LiDAR sensors that are effectively utilized for Visual Odometry (VO) and LiDAR Odometry (LO). Unfortunately, such measurements commonly suffer from outliers in a dynamic environment, since frequently it is assumed that only the robot is in motion and the world is static. To this end, robust state estimation schemes are mandatory in order for humanoids to symbiotically co-exist with humans in their daily dynamic environments. In this article, the robust Gaussian Error-State Kalman Filter for humanoid robot locomotion is presented. The introduced method automatically detects and rejects outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Subsequently, the proposed method is quantitatively and qualitatively assessed in realistic conditions with the full-size humanoid robot WALK-MAN v2.0 and the mini-size humanoid robot NAO to demonstrate its accuracy and robustness when outlier VOLO measurements are present. Finally, in order to reinforce further research endeavours, our implementation is released as an open-source ROS/C++package.

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.

In this paper we propose a novel hybrid walking pattern generator which combines results from the virtual constraints and the preview control theories for bipedal locomotion. This choice is motivated by findings in biomechanics that show how the dynamic motion of the human walk is mainly generated by the sagittal component of the stepping. Thus, we choose the conservative preview control to generate the lateral motion while we pick a more dynamical framework such as the virtual constraints for the sagittal motion. We investigate how the time-dependent preview control and the time-independent virtual constraints approach can be integrated together in a humanoid locomotion and finally we show promising results on COMAN+, the new humanoid robot from Istituto Italiano di Tecnologia.

We present a unified control framework that generates dynamic walking motions for biped and quadruped robots with online relative footstep optimization. The footstep optimization is formulated as a discrete-time Model Predictive Control problem which determines future footstep locations. The framework has a hierarchical structure consisting of three layers: footstep planner, trajectory generator and whole-body controller. The footstep planner plans next footstep position based on Linear Inverted Pendulum (LIP) model. Relative footstep optimization is proposed to enable automatic footstep planning without the use of any predefined footstep sequences. The trajectory generator will generate CoM and feet trajectory given the next footstep placement. In order to generalize to quadruped robots, “virtual leg” concept has been used to coordinate leg pair movement. The whole-body inverse dynamic controller calculates joint torques to track given Cartesian reference trajectories. To include under-actuation into consideration, contact vertices formulation of ground reaction forces (GRFs) has been adopted. Generalized whole-body controller can handle biped robot with line feet as well as quadruped robots with point feet walking with dynamic gaits. Several simulations have been performed to demonstrate the robustness and generality of the proposed framework.

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.

This work presents a new attitude balancing strategy implemented and validated on a quadrupedal robot equipped with a custom hybrid wheel-legged mobility system. The proposed method uses an inverse kinematics solution scheme based on Quadratic Programming optimization to generate full body motions that ensure the desired balancing performances. The strategy generates a compliant behaviour to cope with the applied external forces resulting in a stable and smooth reaction response. Furthermore, the method takes advantage of the robot hybrid wheeled-legged mobility system to provide new motion capabilities and balancing reactions as it will be shown through the paper. Extensive simulation studies on the Centauro robot are presented. Results show the efficiency of the propose method demonstrating significant contribution in the rejection of the applied external disturbances.

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.

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.

This paper investigates the energy efficiency potential of continuously controllable clutches between the motors and links of robot joints. Inspired by biological muscles, the clutch enables free, purely gravity driven robot link motion phases gradually disengaged from gear friction, not requiring motor effort. The concept combines the energetic benefits of direct drives during unforced motion phases with the high torque density of conventional and mature geared robotic drive technology during forced motion phases. The paper specifies the general functional principle of the clutch for energy saving independent of any particular clutch implementation. The feasible energy saving of up to 60 % is investigated for harmonic link motions with varying frequencies and with respect to different ratios of link weight to friction torque. The outcomes of the theoretical investigations are supported by first experimental results.

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 work presents the development of a 3-DoF leg with series and parallel compliant actuation. Series-elastic main actuators are combined with parallel high efficiency energy storage branches, to substantially improve energy efficiency. The leg design is semi-anthropomorphic, with similar mass and mass distribution to the human limb, and includes a biarticulated actuation configuration. The parallel branches are driven by secondary motors and their design parameters are optimised. The mechanical design of the prototype leg is presented, introducing details of the actuation configuration principles employed. Preliminary experimental data are presented, in which a baseline series-elastic-only configuration is compared with configurations with mono- and biarticulated parallel branches, respectively. The results effectively demonstrate the concept's potential, showing improvements of 53% and 60% in electrical power consumption while the leg is executing loaded cyclic motion profiles.

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.

Complex interactions with unstructured environments require the application of appropriate restoring forces in response to the imposed displacements. Impedance control techniques provide effective solutions to achieve this, however, their quasi-static performance is highly dependent on the choice of parameters, i.e. stiffness and damping. In most cases, such parameters are previously selected by robot programmers to achieve a desired response, which limits the adaptation capability of robots to varying task conditions. To improve the generality of interaction planning through task-dependent regulation of the parameters, this paper introduces a novel self-regulating impedance controller. The regulation of the parameters is achieved based on the robot's local sensory data, and on an interaction expectancy value. This value combines the interaction values from the robot state machine and visual feedback, to authorize the autonomous tuning of the impedance parameters in selective Cartesian axes. The effectiveness of the proposed method is validated experimentally in a debris removal task.

Legged-wheeled robots combine the advantages of efficient wheeled mobility with the adaptability to real-world terrains through the legged locomotion. Due to this hybrid mobility skill, they can excel in many application scenarios where other mobile platforms are not suitable for. However, their versatile mobility increases the number of constraints in their motion control where both the properties of legged and wheeled systems need to be considered. Relevant schemes for legged-wheeled platforms so far have been developed exploiting separate motion control of the wheeled and legged functionalities. This paper discusses the legged-wheeled motion kinematics without constraining the camber angles of the wheels, and it proposes a first-order inverse kinematics scheme that stabilizes the legged-wheeled system in the wheeled motion. Furthermore, the work adopts a floating base model that allows to easily incorporate the legged motion to the scheme. The developed controller is tested in simulation and experiments on a legged-wheeled centaur-like robot - CENTAURO.

The present work addresses the generation of a walking gait with automatic footstep placement for a quadrupedal robot, within a Linear Model Predictive Control framework. Existing work has shown how this is only possible within a non-convex programming framework, finding a solution of which is well-known to be very hard. We propose a way to formulate the joint optimization problem as an approximate QP with linear constraints, whose global optimum can be quickly found with off-the-shelf solvers. More specifically, this is done by introducing auxiliary states and control inputs, each of which is subject to linear constraints that are inspired from the literature on bipedal locomotion. Finally, we validate our method on the CENTAURO robot, a hybrid wheeled-legged quadruped with a humanoid upper-body.

Limitations with the on-board computational resources installed on untethered robots such as humanoids and mobile robots in general affects significantly the performance and capabilities of these machines. An approach to address this issue is to make use of the cloud robotics concept and take advantage of the extensive computational resources of the cloud. XBotCloud is a recently developed component of the XBot framework. It tackles the above challenges by introducing the tools and mechanisms to enable users and robots to exploit the computational resources of the cloud allowing the execution of services with low, soft or hard Real-Time execution/communication performance. The latter is ensured thanks to the functionality provided by the XBotCore Real-Time cross-robot software component of the XBot framework. XBotCloud addresses also one of the main challenges related with cloud robotics: security. To avoid remote attacks it takes advantage of the Amazon Web Services (AWS)Cloud Security and it uses an internal VPN Network to handle the connectivity between the robot and the cloud server. The full implementation of the framework is presented and its functionality is demonstrated in realistic tasks involving pipelines that mix the execution of cloud services with moderate execution time constraints and Real-Time modules running on the robot local control unit. XBotCloud performances and cross-robot flexibility are experimentally validated on two different robotic platforms, the WALK-MAN humanoid and the CENTAURO upper body/full-body.

In this paper, we propose a novel method for selective management of muscle fatigue in human-robot co-manipulation. The proposed framework enables the detection of excessive fatigue levels of an individual muscle group while executing a certain task, and provides anticipatory robotic responses to distribute the effort among less-fatigued muscles of human arm. Our approach uses a machine learning technique to enable online predictions of muscle forces in different arm configurations and endpoint interaction forces. The estimated muscle forces are then used for the model-based estimation of muscle fatigue levels. Through optimisation, the fatigue management system can alter the task execution in a way that specific fatigued muscles are offloaded, while at the same time enables the production of task force using muscles with lower levels of fatigue. The main advantage of the proposed method is that it can operate online, and that all the measurements are performed by the robot sensory system, which can significantly increase the applicability in real-world scenarios. To validate the proposed method, we performed proof-of-concept experiments where the task of the human operator was to use a tool to polish an object that was manipulated by the robot.

Collaborative robots are often designed with limited power and force capacity, with the aim to provide affordable solutions and ensure human safety in case of accidental collisions and impacts. If a task requires a power beyond this capacity, or is performed repeatedly over long periods, such limits may be exceeded, which can cause inevitable robot damage and contribute to the lost productivity. In such cases, where hardware solutions and improvements are not applicable, effective software frameworks can prolong robot productivity and lifetime. To this end, in this paper we propose a novel technique for the monitoring and management of robot fatigue in repetitive or high-effort task execution scenarios. The robot fatigue is estimated by the measured temperature of motors in the joints. The proposed fatigue management system is composed of two-stage reaction process that is triggered by different levels of the estimated fatigue. The first stage exploits the kinematic redundancy of robot structure in attempt to minimise the load in the specific joints that under fatigue by reconfiguration in the joint space through the null space of the Cartesian task production. If the first stage is not successful in reducing the fatigue, the second stage is activated that gradually reduces the forces of hybrid controller. At that point, the human co-worker can temporarily take over the task execution until the robot will be recovered from the excessive fatigue. To validate the proposed approach we conducted experiments on KUKA Lightweight Robot performing two interaction tasks: autonomous surface wiping and collaborative human-robot surface polishing.

This paper introduces the design of a novel under-actuated hand with highly integrated modular finger units, which can be easily reconfigured in terms of finger arrangement and number to account for the manipulation needs of different applications. Each finger module is powered by a single actuator through an under-actuated transmission and equipped with a sensory system for delicate and precise grasping, which includes absolute position measurements, contact pressure sensing at finger phalanxes and motor current readings. Finally, intrinsic elasticity integrated in the transmission system make the hand robust and adaptive to impacts when interacting with the objects and environment. This highly integrated hand (HERI II) was developed for the Centauro Robot to enable robust and resilient manipulation. A set of experiments demonstrating the hand's grasping performance were carried out and fully verified the design effectiveness of the proposed hand.

This work presents a method to increase the peak output speed of surface permanent magnet synchronous machine (SPMSM) motor drives with application in robotics using field weakening control. Contrary to most existing works, the strategy is stateless and operates using only a motor torque reference as input, making it suitable for robotics applications in which reference torque and speed are continuously and rapidly changing. Based on the system dynamics and constraints, we obtain four different operating modes. The strategy is extensively validated using three different experiments, which show an increase in peak velocity of up to 33%. The results demonstrate that the proposed strategy is effective in extending the dynamic performance and explosive motion capabilities of robots.

This paper proposes the implementation of an impedance controller on the ankle level of COMAN+, a robot with parallel kinematics ankles actuated by a dual four-bar mechanism. The main contribution of the work is a realization of said control scheme that grants a less abrupt and safer robot response in case of system failures, that would cause the local joint torque controllers to lose their torque reference inputs. In particular, we propose a semi-centralized impedance control implementation which eliminates the instability of the pure joint torque control schemes used in the classical fully centralized methods when torque reference interruptions occur. Finally, we present experimental results, proving the effectiveness of our method and demonstrating how it ensures a safer behaviour compared to a fully centralized impedance control implementation when the communication to the ankle joints is interrupted. This paper is a follow-up work of [1], which presented and analyzed the parallel kinematics ankles.

This work presents a novel method for estimating online the torques at the ankle, knee and hip of a user with the goal of generating reference signals for torque controlled lower limb exoskeletons. In particular, this approach attempts to address difficulties arising in real life scenarios when noncyclic locomotion activity, unexpected terrain or unpredicted interactions with the surroundings occur. An advantage of the proposed method is that it does not require any information on the user's upper body (i.e. pose, weight and center of mass location)or on any interaction of the user's upper body with the environment (i.e. payload handling or pushing and pulling task). By monitoring the interaction of the user's feet with the ground through a novel sensorized shoe sensing system, the method applies an inverse static analysis on the user's lower limbs to estimate in real time the torque at each leg joint. The system is fully wearable, ergonomic and portable and uses a reduced number of body posture sensors. The design of the sensorized shoes permits plantar flexion, while measuring the toe and heel orientation and the interaction loads. This allows walking on irregular terrains and natural feet postures in different tasks. Trials were performed to validate the proposed approach under different tasks and terrains. Finally, the knee torques estimated online by the proposed strategy were used as reference signals to drive the iT-Knee Bipedal System in an assistive task.

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.

To generate dynamic motions such as hopping and running on legged robots, model-based approaches are usually used to embed the well studied spring-loaded inverted pendulum (SLIP) model into the whole-body robot. In producing controlled SLIP-like behaviors, existing methods either suffer from online incompatibility or resort to classical interpolations based on lookup tables. Alternatively, this paper presents the application of a data-driven approach which obviates the need for solving the inverse of the running return map online. Specifically, a deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics. The trained network is applied online to generate reference foot placements for the humanoid robot. The references are then mapped to the whole-body model through a QP-based inverse dynamics controller. Simulation experiments on the WALK-MAN robot are conducted to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions.

This paper aims at the development of a unified method for online identification and tracking of a kinematic musculoskeletal model of human arm to pave the way for related realtime applications, such as human-robot interaction, teleoperation and biomedical analysis. In order to decouple the identification of the joint angles of human arm kinematic model from a variety of motion capture (MoCap) setups, a generalized human arm triangle, which can be easily calculated by raw motion data, is introduced as an intermediate unified expression interface of human arm posture. An analytical solution to the Inverse Kinematics (IK) problem from the proposed human arm triangle to the joint angles of a commonly used OpenSim human right arm model is derived in detail. Once the human arm kinematic model is reconstructed, the involved muscles can be located correspondingly for related applications. Comparative simulation and experiment are conducted to validate the performance of the proposed IK and the whole tracking method. The results manifest that the calculation efficiency of the proposed IK can achieve an enormous speedup of 400-600 times with respect to the OpenSim built-in IK while maintaining comparable accuracy. Therefore, the proposed method can be an important tool to enable many online applications using human arm musculoskeletal model.

The understanding of human arm stiffness have brought several significant advances to robotics. For the most part, the end-point stiffness of human arm serves as an important role in guiding and shaping the Cartesian stiffness of robot arm in the execution of complicated interaction tasks because of the convenience of using the common space where both stiffnesses function. However, investigation of the joint stiffness of human arm, on the other hand, will provide a more comprehensive perspective on the human arm stiffness and enable other appealing robotic applications, for instance, whole-arm interaction with unstructured environment. As a fundamental research for these applications, the feasibility of an online joint stiffness transfer approach from human to anthropomorphic arms is discussed in this paper. This is realized by a proposed concept of physiological joint stiffness, which is shared by the human and anthropomorphic arms. The desired joint stiffness of robot arm is transformed from the estimated joint stiffness of human arm by requiring both arms to have the same apparent physiological joint stiffness. To make the calculated joint stiffness achievable in a robot controller, the stiffness matrix is subsequently optimized to be symmetric and positive definite. Proof-of-concept experiment is performed on a fully integrated robotic teleoperation setup to validate the efficacy of the proposed method.

In this paper, we present a novel global tracking control approach for elastic-joint robots that can be efficiently computed and is robust against model uncertainties and input disturbances. Elastic-joint robots provide enhanced safety and resiliency for interaction with the environment and humans. On the other hand, the joint elasticity complicates the motion-control problem especially when robust and precise trajectory tracking is required. Our proposed control approach allows us to merge the main benefits of the two well-known control schemes: inverse-dynamics (ID) control, which can be efficiently computed thanks to modern recursive algorithms, and passivity-based (PB) tracking control, which provides enhanced robustness to model uncertainty and external disturbances. As an extension of our previous work, we present a detailed robustness analysis of our combined ID/PB controller, a new variant of the original scheme that shows practically relevant implications, and finally, experimental results that verify the effectiveness of the approach.

We consider the global tracking control problem of robots with elastic joints. Even if joint elasticity introduces beneficial features for modern applications which require physically resilient and safer robots that can interact with the environment or humans, it challenges the achievable control performance. We propose a novel controller which combines the benefits of two approaches: the intrinsic robustness to model uncertainty from passivity-based control and the implementation efficiency of inverse-dynamics control schemes using a modern recursive algorithm. The novel controller is applied to an elastic-joint reconfigurable robotic arm using a recently proposed framework for on-the-fly control design. Simulation and experimental results validate our proposed approach.

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.

This paper offers a novel insight of using nonlinear models for the control to produce more robust and natural walking gaits for humanoid robots. The sagittal and lateral gait control needs to be treated differently, hence, we proposed two types of suitable nonlinear models, which allow forward simulations to look ahead, and thus, predict accurately the future trajectory/state at the end of the current step. Subsequently, by performing multiple forward simulations in a similar manner for the next step and using the gradient descent method, an appropriate foot placement can be found to achieve precise walking speed. By doing this two-step lookahead, all trajectories of the support and the swing leg can be generated. Our proposed controller can plan trajectories at the beginning of each step or actively re-plan according to task state errors. It is validated effectively in simulations performed in both ADAMS and Open Dynamic Engine. The robot can successfully traverse up/down a stair and recover from pushes with more natural looking gaits compared to the conventional bent-knee style. The reasonable computational time also indicates the feasibility of real-time implementation on real robots.

Developing a high physical performance robotic manipulation platform with considerable power density, strength and resilience is not a trivial task and frequently leads to heavy and bulky systems unable to meet the application requirements, i.e. such robots should have human body size compatibility to work in infrastructures designed for humans. In this work we present a new high performance human size and weight compatible bi-manual manipulation platform that demonstrates notable physical strength and power capabilities. To attain this performance, design features including custom high performance elastic drives and robust light weight structure principles were considered resulting in large payload to robot mass ratio that is greater than 1.5 for short time heavy payloads. The design principles and mechanics of the upper body bi-manual robot are presented providing details on the solutions adopted for the various mechatronics components. The performance of the system actuation and the strength capacity of the overall platform is verified through the execution of heavy payload motion and impact experiments.

Robotic teleoperation in cluttered environments is attracting increasing attention for its potential in hazardous scenarios, disaster response, and telemaintenance. Although haptic feedback has been proven effective in such applications, commercially-available grounded haptic interfaces still show significant limitations in terms of workspace, safety, transparency, and encumbrance. For this reason, we present a novel robotic teleoperation system with wearable haptic feedback for telemanipulation in cluttered environments. The slave system is composed of a soft robotic hand attached to a 6-axis force sensor, which is fixed to a 6-degrees-of-freedom robotic arm. The master system is composed of two wearable vibrotactile armbands and a Leap Motion. The armbands are worn on the upper arm and forearm, and convey information about collisions on the robotic arm and hand, respectively. The position of the manipulator and the grasping configuration of the robotic hand are controlled by the user's hand pose as tracked by the Leap Motion. To validate our approach, we carried out a human-subject telemanipulation experiment in a cluttered scenario. Twelve participants were asked to teleoperate the robot to grasp an object hidden between debris of various shapes and stiffnesses. Haptic feedback provided by our wearable devices significantly improved the performance of the considered telemanipulation tasks. All subjects but one preferred conditions with wearable haptic feedback.

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.

In this paper, the Hardware Embedded Reduced Intricacy (HERI) Hand, which is a novel tendon driven three-finger under-actuated hand demonstrating balanced dexterous finger manipulation and powerful grasping of common objects is presented. The third finger of HERI Hand is asymmetrically designed in terms of dimensions to emulate the functionality for combining middle finger, ring finger and little finger of a human hand. HERI Hand is equipped with three actuators devoted to the actuation of the flexion of the index finger and thumb, the flexion of the third finger and finally the thumb abduction and adduction motion with the latest drive having no interference with other transmissions. The proposed hand is capable of realizing delicate finger manipulation such as opening a lidded cup, which is super suitable to accomplish in such configuration. At the same time the hand demonstrates high grasping strength capacity thanks to the actuation sizing permitted by the under-actuated configuration. The hand is also equipped with tactile/pressure sensors distributed in the phalanxes, which leaves open the possibility of potential applications for sophisticated finger manipulations taking into account the phalanxes contact forces. Three different sets of experiments were carried out to demonstrate the performance of HERI Hand and validated its functionality.

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.

This paper is motivated by the questions: What would happen if a humanoid robot is put on a Segway? Is it possible for the humanoid robot to use this transportation device that is specifically designed for human? Simulation involving a two-wheeled mobile platform (TWMP) and our humanoid robot COMAN (COmpliant HuMANoid Platform) shows that it is indeed feasible without any hardware modification. Regarding the implementation, the full dynamics of the humanoid robot is considered and quadratic optimization is employed to generate whole-body joint torques to realise two types of tasks according to the interaction type between the TWMP and the humanoid robot. The TWMP is considered as unknown disturbance and the humanoid robot has to keep balancing on it in the first type of task. On the contrary, the active movement of the humanoid robot is utilised as an interface to intuitively drive the TWMP in the second type of task. For both tasks, tracking the position of center of mass (CoM) and regulating the angular momentum around it are considered as primary objectives, stabilizing the posture of certain part of its body is optional. In addition, both tasks are repeated on uneven terrain to demonstrate the robustness of the control method.

In humanoids and other redundant robots interacting with the environment, one can often choose between different configurations and control parameters to achieve a given task. A classic tool to describe specifications of the desired force/displacement behavior in such problems is the stiffness ellipsoid, whose geometry is affected by the choice of parameters in both joint control and redundancy resolution-namely, gains and angles. As is well known, impedance control techniques can regulate gains to realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector, so that robot geometry selection could appear secondary. However, humans do not use this possibility: To control the stiffness of our arms, we predominantly use arm configurations. Why is that, and does it makes sense to do the same in robots? To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of compliant redundant arms to illustrate why the arm geometry plays a dominant role in interaction capabilities of robots. We introduce the notion of allowable Cartesian force/displacement (“stiffness feasibility”) regions (SFR) for compliant robots with given torque boundaries. We show that different robot configurations modify such regions and explore the role of robot geometry in achieving an appropriate SFR for the task at hand. The novel concepts and definitions are first illustrated in simulations. Experimental results are then provided to verify the effectiveness of the proposed Cartesian force and stiffness control.

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, to guarantee the Pisa/IIT SoftHand's grasp robustness against slippage, three reflex control modes, namely Current, Pose and Impedance, are implemented and experimentally evaluated. Towards this objective, ThimbleSense fingertip sensors are designed and integrated into the thumb and middle fingers of the SoftHand for real-time detection and control of the slippage. Current reflex regulates the restoring grasp forces of the hand by modulating the motor's current profile according to an update law. Pose and Impedance reflex modes instead replicate this behaviour by implementing an impedance control scheme. The difference between the two latter is that the stiffness gain in Impedance reflex mode is being varied in addition to the hand pose, as a function of the slippage on the fingertips. Experimental setup also includes a seven degrees-of-freedom robotic arm to realize consistent trajectories (e.g. lifting) among three control modes for the sake of comparison. Different test objects are considered to evaluate the efficacy of the proposed reflex modes in our experimental setup. Results suggest that task-appropriate restoring forces can be achieved using Impedance reflex due to its capability in demonstrating instantaneous and rather smooth reflexive behaviour during slippage. Preliminary experiments on five healthy human subjects provide evidence on the similarity of the control concepts exploited by the humans and the one realized by the Impedance reflex, highlighting its potential in prosthetic applications.

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.

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.

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 paper presents a novel approach for human-robot cooperation in tasks with dynamic uncertainties. The essential element of the proposed method is a multi-modal interface that provides the robot with the feedback about the human motor behaviour in real-time. The human muscle activity measurements and the arm force manipulability properties encode the information about the motion and impedance, and the intended configuration of the task frame, respectively. Through this human-in-the-loop framework, the developed hybrid controller of the robot can adapt its actions to provide the desired motion and impedance regulation in different phases of the cooperative task. We experimentally evaluate the proposed approach in a two-person sawing task that requires an appropriate complementary behaviour from the two agents.

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.

This work presents the development of iT-Knee, a novel modular knee exoskeleton that makes use of a torque transmission interface to deliver pure assistive torque to the knee articulation, with the objective to act as rehabilitation tool, or a single joint add-on exoskeleton device for power augmentation. The specific kinematics features employed by iT-Knee can accommodate not only the translational of the knee instantaneous center of rotation in the sagittal plane and on its orthogonal direction, but also the knee varus/valgus angle as well as the internal/external rotation tibia movements delivering pure torque only around the flexion/extension movement. This functionality results in superior transparency and comfortability which allow prolonged period of use without generating significant discomfort. The iT-Knee kinematics, its mechanism, implementation, and actuation are introduced. The Self-aligning feature combined with a fast lock/release mounting system on the human leg shortens the setup time required to wear it, increasing its user-friendly perception. iT-Knee uses a torque controlled actuation unit to generate torque around the knee flexion extension enabling the implementation of several interaction control schemes that combine assistive functionality on demand with high back drivability and transparency during unloaded free motions of the human knee. Experimental results are presented and effectively demonstrate the functionality of the iT-Knee device.

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.

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.

The stiffness ellipsoid, i.e. the locus of task-space forces obtained corresponding to a deformation of unit norm in different directions, has been extensively used as a powerful representation of robot interaction capabilities. The size and shape of the stiffness ellipsoid at a given end-effector posture are influenced by both joint control parameters and - for redundant manipulators - by the chosen redundancy resolution configuration. As is well known, impedance control techniques ideally provide control parameters which realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector in an arbitrary non-singular configuration, so that arm geometry selection could appear secondary. This definitely contrasts with observations on how humans control their arm stiffness, who in fact appear to predominantly use arm configurations to shape the stiffness ellipsoid. To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of redundant arms, which explains why arm geometry also plays a fundamental role in interaction capabilities of a torque controlled robot. We show that stiffness control of realistic robot models with bounds on joint torques can't indeed achieve arbitrary stiffness ellipsoids at any given arm configuration. We first introduce the notion of maximum allowable Cartesian force/displacement (“stiffness feasibility”) regions for a compliant robot. We show that different robot configurations modify such regions, and explore the role of different configurations in defining the performance limits of Cartesian stiffness controllers. On these bases, we design a stiffness control method that suitably exploits both joint control parameters and redundancy resolution to achieve desired task-space interaction behavior.

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.

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.

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.

Effective and stable execution of a remote manipulation task in an uncertain environment requires that the task force and position trajectories of the slave robot be appropriately commanded. To achieve this goal, in teleimpedance control, a reference command which consists of the stiffness and position profiles of the master is computed and realized by the compliant slave robot in real-time. This highlights the need for a suitable and computationally efficient tracking of the human limb stiffness profile in real-time. In this direction, based on the observations in human neuromotor control which give evidence on the predominant use of the arm configuration in directional adjustments of the endpoint stiffness profile, and the role of muscular co-activations which contribute to a coordinated regulation of the task stiffness in all directions, we propose a novel and computationally efficient model of the arm endpoint stiffness behaviour. Real-time tracking of the human arm kinematics is achieved using an arm triangle monitored by three markers placed at the shoulder, elbow and wrist level. In addition, a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. Calibration and identification of the model parameters are carried out experimentally, using perturbation-based arm endpoint stiffness measurements in different arm configurations and co-contraction levels of the chosen muscles. Results of this study suggest that the proposed model enables the master to naturally execute a remote task by modulating the direction of the major axes of the endpoint stiffness and its volume using arm configuration and the co-activation of the involved muscles, respectively.

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.

In this paper we present a novel whole-body compliance framework. It is based on the Multi Spring Model, a set of virtual springs interconnecting the limb extremities and the trunk as well. More specifically, six virtual springs connect the trunk of the robot to the feet and the hands and other six springs interconnect the limb extremities. By selecting the stiffness values of each individual spring, complex behaviors can be generated, opening the way for more challenging tasks. The framework is implemented in a multi-layer architecture that distributes the computation on the different control units available on the robot. The proposed method was tested in two different tasks using the COmpliant huMANoid (COMAN), a full-body torque-controlled humanoid robot. For the first task, the robot was able to grasp a box, lift it up and firmly hold it. In the second task, the robot balanced itself while having the hands in contact with two walls. COMAN was able to successfully perform both tasks while perturbed by external disturbances and environmental uncertainties.

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.

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.

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.

Incorporation of human motor control principles in the motion control architectures for humanoid robots or assistive and prosthesis devices will permit these systems not only to look anthropomorphic and natural at the body ware level but also to generate natural motion profiles resembling those executed by humans during manipulation and locomotion. In this work, relying on the observations on human bimanual coordination, a novel realtime motion control strategy is proposed to regulate the desired Cartesian stiffness profile during the execution of bimanual tasks. The novelty of the proposed control scheme relies on the use of common mode stiffness (CMS) and configuration dependent stiffness (CDS) to regulate the size and directionality of the task space stiffness ellipsoid. Thanks to the CDS control, the proposed scheme is not only proved to be effective in regulating the desired stiffness ellipsoid but also permits to resolve the manipulator redundancy in a natural manner. The effectiveness of the controller is evaluated in an experimental setup in which two cooperating robotic arms are executing an assembly task. Experimental results demonstrate that the proposed dual-arm CDS-CMS controller is effective in tracking the desired stiffness ellipsoids as well as in producing human-like natural motions for the two robotic arms.

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.

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.

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.

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.

In this paper we report on the development of a modular multi-DoF F/T sensor and its use in the implementation of a sensorized object capable of multi-touch detection. The sensor is composed of six 6-axis F/T sensors spatially organized on the faces of a cube. Different calibration methods are presented to directly tackle the coupling phenomena inherent to the spatial organization of the faces and the lightweight construction of the sensor which would have, otherwise, degraded its accuracy. To assess the performances of the calibration methods, a comparison is reported with respect to the measurements obtained with a commercial force/torque sensor considered as ground truth (ATI Delta). Thanks to the modular design and the possibility to cover the sensitive faces with surface patches of different geometry, a variety of sensorized objects with different shapes can be realized. The peculiar feature that all the components of the contact wrench can be measured on each face with high accuracy, renders it a unique tool in the study of grasp force distribution in humans, with envisioned use both in neuroscience investigations and robotic applications.

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.

Humans incorporate and switch between learnt neuromotor strategies while performing complex tasks. Towards this purpose, kinematic redundancy is exploited in order to achieve optimized performance. Inspired by the superior motor skills of humans, in this paper, we investigate a combined free motion and interaction controller in a certain class of robotic manipulation. In this bimodal controller, kinematic degrees of redundancy are adapted according to task-suitable dynamic costs. The proposed algorithm attributes high priority to minimum-effort controller while performing point to point free space movements. Once the robot comes in contact with the environment, the Tele-Impedance, common mode and configuration dependent stiffness (CMS-CDS) controller will replicate the human's estimated endpoint stiffness and measured equilibrium position profiles in the slave robotic arm, in real-time. Results of the proposed controller in contact with the environment are compared with the ones derived from Tele-Impedance implemented using torque based classical Cartesian stiffness control. The minimum-effort and interaction performance achieved highlights the possibility of adopting human-like and sophisticated strategies in humanoid robots or the ones with adequate degrees of redundancy, in order to accomplish tasks in a certain class of robotic manipulation.

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.

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.

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.

In this paper, a novel synergy driven teleimpedance controller for the Pisa-IIT SoftHand is presented. Towards the development of an efficient, robust, and low-cost hand prothesis, the Pisa-IIT SoftHand is built on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. As the SoftHand grasps, it follows a synergistic path with built-in flexibility to allow grasping of objects of various shapes using only a single motor. In this work, the hand grasping motion is regulated with an impedance controller which incorporates the user's postural and stiffness synergy profiles in realtime. In addition, a disturbance observer is realized which estimates the grasping contact force. The estimated force is then fedback to the user via a vibration motor. Grasp robustness and transparency improvements were evaluated on two healthy subjects while grasping different objects. Implementation of the proposed teleimpedance controller led to the execution of stable grasps by controlling the grasping forces, via modulation of hand compliance. In addition, utilization of the vibrotactile feedback resulted in reduced physical load on the user. While these results need to be validated with amputees, they provide evidence that a low-cost, robust hand employing hardware-based synergies is a viable alternative to traditional myoelectric prostheses.

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.

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.

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.

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.

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.

This work presents the novel concept of Tele-Impedance as a method for controlling/teleoperating a robotic arm while performing tasks which require significant dynamics variation. As an alternative method to bilateral force-reflecting teleoperation control approach, which uses a position/velocity command combined with force feedback from the robot side, Tele-Impedance enriches the command sent to the slave robot by combining the position reference with a stiffness (or full impedance) reference estimated from the arm of the human operator. We propose a new method to estimate the stiffness of the human arm based on the agonist-antagonist muscular co activations. The concept of the Tele-Impedance is demonstrated using the KUKA light weight robotic arm as the slave manipulator in a ball reception experiment. The performance of Tele-Impedance control method is assessed by comparing the results obtained while receiving the ball, with the slave arm under i) constant low stiffness, ii) constant high stiffness or iii) under Tele-Impedance control. Performance indexes are defined and used for the comparative study of the ball reception performances under the different endpoint elastic profiles. The experimental results demonstrate the effectiveness of the task-related Tele-Impedance control method and highlight its potential use to execute tasks which require significant dynamics variation.

Recent robotic research recognized the advantages that Variable Impedance Actuators would yield to a new generation of robots, rendering them adapt to many different tasks of everyday life.

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.

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.

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.

In this paper a simple tracking controller for a variable stiffness joint is proposed. System dynamics is considered unknown. The controller guarantees link and stiffness motor position performance specifications that have been apriori set, utilizing full state feedback. Simulation results on the previously published CompAct-VSA joint validate the efficiency of the proposed control approach.

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.

We propose a prototype of a Variable Stiffness Actuator (VSA) conceived with low cost as its first goal. This approach was scarcely covered in past literature. Many recent works introduced a large number of actuators with adjustable stiffness, optimized for a wide set of applications. They cover a broad range of design possibilities, but their availability is still limited to small quantities. This work presents the design and implementation of a modular servo-VSA multi-unit system, called VSA-CubeBot. It offers a customizable platform for the realization and test of variable stiffness robotic structures with many degrees of freedom. We present solutions relative to the variable stiffness mechanism, embedded electronics, mechanical and electrical interconnections. Characteristics, both theoretic and experimental, of the single actuator are reported and, finally, five units are interconnected to form a single arm, to give an example of the many possible applications of this modular VSA actuation unit.

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.

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.

This paper focuses on the estimation of the impedance for a Variable Impedance Actuator (VIA) through torque and position measurements. Despite the recent development of several VIA, impedance control is not yet implemented in closed loop because of the difficulty of obtaining in real-time measurements of time-varying impedance. The estimation algorithm is proposed as an alternative approach to the standard procedures of impedance identification, to robustly tolerate the variability of the mechanical stiffness due, for example, to model uncertainties. The impedance estimator is therefore implemented on the Actuator with Adjustable Stiffness (AwAS). The effectiveness of the proposed estimator is proved through simulation and experimental results.

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.

Affine-invariant feature matching plays an important role in many robot vision applications, such as robot visual navigation, object detection, visual tracking and visual SLAM, etc. In the early stages, invariant keypoints are used to detect the affine transformation. But the accuracy is very low. In recent years, some people introduce SIFT method into robot vision field, which greatly enhances the accuracy. But it is too time-consuming to meet the requirements of real-time robot vision applications. In this paper, we propose a novel learning-based feature matching approach to address the problem. First, it uses a fast algorithm to extract keypoints. Then, our method identifies keypoints that belong to different objects or background by color and texture representation. The keypoints are clustered into corresponding groups. At last, a two-stage multilayer ferns classifier is trained to recognize the local patches and get the estimate of viewpoint. We test our approach on public datasets and apply it in a visual SLAM application. The result demonstrates that our method can provide robust and powerful matching ability. Even on some difficult matching cases, it also performs remarkably well. Further more, because there is no need to compute descriptors for the image, our method is very fast at run-time.

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.

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.

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 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.

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 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.

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.

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.

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.

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.

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.