While legged robots have achieved significant advancements in recent years, ensuring the robustness of their controllers on unstructured terrains remains challenging. It requires generating diverse and challenging unstructured terrains to test the robot and discover its vulnerabilities. This topic remains underexplored in the literature. This paper presents a Quality-Diversity framework to generate diverse and challenging terrains that uncover weaknesses in legged robot controllers. Our method, applied to both simulated bipedal and quadruped robots, produces an archive of terrains optimized to challenge the controller in different ways. Quantitative and qualitative analyses show that the generated archive effectively contains terrains that the robots struggled to traverse, presenting different failure modes. Interesting results were observed, including failure cases that were not necessarily expected. Experiments show that the generated terrains can also be used to improve RL-based controllers.

For successful peg-in-hole assembly, predefined sub-tasks should be executed sequentially according to the current contact state. Therefore, recognizing contact state transitions is essential in order to determine whether to continue the current task or proceed to the next. In that context, learning-based solutions have shown outstanding results. However, these methods heavily rely on balanced datasets, which are challenging to obtain due to the short duration of certain contact states and rare failure cases. To address this issue, this paper proposes a framework for estimating contact state transitions using anomaly detection through input data reconstruction. The proposed framework operates in a semi-supervised manner, eliminating the need for balanced datasets during training. For input data reconstruction, a convolutional neural network is combined with a variational autoencoder to process various sensor measurements as a multivariate time series. Unlike traditional binary anomaly detection, the proposed anomaly detector scores reconstruction errors and leverages domain knowledge to identify various contact state transitions in the peg-in-hole assembly. The effectiveness of the proposed framework is validated through experiments using a torque-controlled dual manipulator system.

In this paper, as an approach to stabilize humanoid walking where the height of CoM varies, a Novel Model Predictive Control framework based on three dimensional Divergent Component of Motion (3D-DCM) is proposed. To ensure the feasible utilization of contact forces for maintaining humanoid balance, constraints on the control inputs, Virtual Repellent Point (VRP) and footstep adjustment, and their correlation are analytically formulated into a quadratic form, resulting a Quadratically Constrained Quadratic Programming. Additionally, to enable the humanoid robot to withstand disturbances over a broader range of strides or safely navigates various terrains without encountering knee stretch, the distance between the CoM and the foot is constrained in the 3D-CoM trajectory planner. The effectiveness of the proposed method is validated through simulations and real-robot experiments in scenarios involving external disturbances and step down.

This paper presents an active control strategy for lower limb exoskeletons by proposing an end-to-end framework employing deep reinforcement learning (DRL) to enable smooth transitions between different movement patterns. The majority of existing methods in exoskeleton literature employ finite state machines (FSM) that have proven successful in predicting the control strategy for the next state on the basis of sensor data such as IMU data, force, etc. However, one drawback of FSM occurs due to their inflexibility regarding sudden changes. Specifically, FSM is based on clear state transitions, which makes it hard to manage smooth continuous movements and increases the chance of sudden changes in control during transitions. These, in turn, raise safety concerns for the user. While learning-based control approaches have been suggested in recent years, the validation was performed in simulation environments. Therefore, the real-world applicability remains an open research question to date. To address this issue, we provide the first contribution in this field that proposes an end-to-end learning framework with a Deep Deterministic Policy Gradient (DDPG) module to enable smooth transitions between movement patterns under real-world conditions. By introducing several evaluation metrics, we demonstrate that our framework outperforms existing methods in terms of the adaptability and smoothness in movement transitions.

This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-collisions in dynamic environments at 100 Hz. Many path-following methods rely on the time parametrization, but struggle to handle collision and singularity avoidance while adhering kinematic limits or other constraints. Specifically, the error between the desired path and the actual position can become large when executing evasive maneuvers. Thus, this paper derives a method that parametrizes the reference path by a path parameter and performs the optimization via RMPCC. In particular, Control Barrier Functions (CBFs) are introduced to avoid collisions and singularities in dynamic environments. A Jacobian-based linearization and Gauss-Newton Hessian approximation enable solving the nonlinear RMPCC problem at 100 Hz, outperforming state-of-the-art methods by a factor of 10. Experiments confirm that the framework handles dynamic obstacles in real-world settings with low contouring error and low robot acceleration.

Real-time sampling-based planners increasingly use learned sampling distributions for faster planning in autonomous vehicles. These planners employ a neural network to predict the optimal path and bias some samples toward the path. However, inherent prediction inaccuracies of the network often lead to suboptimal paths, especially in narrow spaces. Learned samples should be used carefully based on accuracy, as inaccurate samples can degrade planning performance. To address this problem, this paper proposesLearned Adaptive Anytime TargetTree-RRT* (LA3T*)algorithm. The proposed planner introduces the adaptive biasing ratio. The approach learns to assess the reliability of the learned distribution using the network’s confidence. This confidence approximates a proper ratio of learned samples used, thereby adaptively maximizing planning performance while considering a level of prediction accuracy. Furthermore, the LA3T* algorithm incorporates the target tree algorithm. The goal pose is replaced with a set (target tree) of pre-defined optimal path segments, reducing computational efforts in narrow regions. Experiments in various driving tasks explore the benefits of each component through ablation studies. The proposed algorithm significantly increases the success rate and reduces the path length in simulated and real-world scenarios compared to other sampling-based methods.

The robust balancing capability of humanoids is essential for mobility in real environments. Many studies focus on implementing human-inspired ankle, hip, and stepping strategies to achieve human-level balance. In this paper, a robust balance control framework for humanoids is proposed. Firstly, a Model Predictive Control (MPC) framework is proposed for Capture Point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. Additionally, a variable weighting method is introduced that adjusts the weighting parameters of the Centroidal Angular Momentum damping control. Secondly, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated compared to a state-of-the-art Quadratic Programming-based CP controller that employs the ankle, hip, and stepping strategies.

The avatar robot is a robot capable of realistic remote operation. In remote operation, the controllability of the glove is crucial. This glove can manipulate the hand interacting directly with the environment at the remote site. The glove must be able to accurately estimate the hand posture and provide haptic feedback to convey information about the remote environment and enhance operability. Throughout the process, user discomfort should be minimized. To achieve this goal, the research proposes providing force feedback to the fingers using Trigonometric Series Elastic Actuators. Haptic gloves are attached to the Middle Phalanx to facilitate the easy installation of additional add-ons, ensuring users feel securely fixed when attached. Additionally, by proposing an algorithm to estimate the fingertip position without directly attaching it to the fingertip, the haptic glove estimates hand posture and delivers appropriate force as needed. Finally, the system, including the haptic glove, participated in the ANA Avatar XPRIZE competition. The avatar system performed eight missions, which included not only remote manipulation of objects but also social interactions, demonstrating its effectiveness.

Rapidly-exploring random tree (RRT) has been studied for autonomous parking as it quickly finds an initial path and is easily scalable in complex environments. However, the planning time increases by searching for the path in narrow parking spots. To reduce the planning time, the target tree algorithm, which substitutes a parking goal in RRT with a set (target tree) of backward parking paths, was proposed. However, as it consists of circular and straight paths, it deteriorates parking accuracy because of curvature-discontinuity. Moreover, the planning time increases in complex environments; backward paths can be blocked by obstacles. Therefore, this paper introduces the TargetTree-RRT* algorithm for complex environments. First, a target tree is designed using clothoid paths to address such curvature-discontinuity. Second, to reduce the planning time further, a cost function is defined to initialize a proper target tree that considers obstacles. By integrating with optimal-variant RRT and searching for the shortest path, the proposed TargetTree-RRT* algorithm obtains a near-optimal path as the sampling time increases. Experiment results in real environments showed that the vehicle parked more accurately, and continuous-curvature paths were obtained more quickly and with higher success rates than those acquired using other sampling-based and other types of planning algorithms. Note to Practitioners—This work was motivated by the need to develop a fast and practical path planning algorithm for autonomous parking, not only in simple environments but also in complex environments. Sampling-based planning algorithms have been studied in this area, with the advantages of rapid planning of an initial path and easily reflecting vehicle’s constraints. Nevertheless, in cluttered environments, it needs considerable time to obtain a path for the autonomous vehicle easy for tracking and precise parking. To address the aforementioned issue, this article presents the TargetTree-RRT* algorithm. It finds a path by replacing a parking goal with a set of pre-defined continuous-curvature paths considering cluttered environments. TargetTree-RRT* achieves better performance for real parking scenarios and rapidly obtains a near-optimal parking path, compared to other parking path planners. The proposed algorithm is not limited to autonomous vehicles. It can be applied to other unmanned vehicles or robots, such as autonomous underwater vehicles (AUVs). In any application where the goal of path planning can be replaced with predefined standardized paths, the proposed algorithm can be extended.

Recently, foot stepping strategies of humanoid robots have been actively developed for robust balancing of humanoids against disturbances. In this paper, a novel stepping algorithm adjusting double support phase (DSP) time is proposed. First, the stepping algorithm is proposed based on a model predictive control (MPC) framework for capture point (CP) control and footstep adjustment. Next, when the remaining step time is not enough to adjust the footstep, the DSP scaling method brings the next swing phase forward by reducing the DSP time, which enables the robot to maintain the balance robustly. The robust balance control performance of the proposed method is validated through simulations and experiments when the robot is walking in the presence of external pushes. A more stable balancing performance is realized compared to state-of-the-art stepping controllers.

This study proposes a realtime motion planning framework that leverages the prediction of a portion of the optimal trajectory for sampling-based anytime planning algorithms. Existing algorithms predict the entire optimal path and bias random samples toward it for fast path planning. However, these algorithms may not be suitable for realtime frameworks because the bias-sampling strategy should consider the sequential nature of realtime execution. Therefore, the proposed algorithm predicts a portion of the optimal path, known as the committed trajectory, step by step as a probability distribution using a neural network. This distribution is then used in a sampling-based anytime planning algorithm as a non-stationary way of biasing random samples. The proposed algorithm can sequentially plan the near-optimal motion, al-lowing the vehicle to reach the desired goal pose in a timely and accurate manner. In various test parking scenarios, the proposed algorithm reduces the parking time by approximately 38% compared with conventional motion planning algorithms and by 10% compared with another realtime framework that biases samples toward the entire optimal trajectory.

The estimation of external joint torque and contact wrench is essential for achieving stable locomotion of humanoids and safety-oriented robots. Although the contact wrench on the foot of humanoids can be measured using a force-torque sensor (FTS), FTS increases the cost, inertia, complexity, and failure possibility of the system. This paper introduces a method for learning external joint torque solely using proprioceptive sensors (encoders and IMUs) for a floating base robot. For learning, the GRU network is used and random walking data is collected. Real robot experiments demonstrate that the network can estimate the external torque and contact wrench with significantly smaller errors compared to the model-based method, momentum observer (MOB) with friction modeling. The study also validates that the estimated contact wrench can be utilized for zero moment point (ZMP) feedback control, enabling stable walking. Moreover, even when the robot's feet and the inertia of the upper body are changed, the trained network shows consistent performance with a model-based calibration. This result demonstrates the possibility of removing FTS on the robot, which reduces the disadvantages of hardware sensors.

The target-tree*algorithm, which is a variant of the optimal rapidly-exploring random tree (RRT*) has been proposed to reduce the parking path planning time. This algorithm pre-generates a set of backward paths (target-tree) around a parking spot and extends an RRT*from the initial pose until it is connected to a random sample of the target-tree. However, it is difficult to obtain the shortest (optimal) parking path within a short planning time because connected samples between the tree and the target-tree are randomly searched. To deal with this problem, this paper proposes a biased target-tree*algorithm with RRT*that searches connected random samples in a biased range near the target-tree. This range has a Gaussian distribution centered on the optimal connected sample where the shortest parking path can be obtained quickly and is obtained through supervised learning. In actual parking situations, the biased target-tree*algorithm obtained a shorter path with less length deviation than the original target-tree*algorithm within a shorter planning time.

Stable contact and safe responses to the collision have been studied to develop interactive robots such as service and collaborative robots. Stable and safe interactions are usually achieved through the inherent compliance of a motion controller with external torque estimation. However, a fixed control gain would sacrifice either compliance or position tracking performance. Additionally, external torque estimation is susceptible to model errors. In this study, a novel variable stiffness control approach is proposed to achieve a high position tracking performance in free motion and compliant behavior in the contact state. For this purpose, a precise estimation of the external torque and control gains that change based on the external torque are required. To estimate the external torque precisely, a collision detecting learning algorithm that uses long short-term memory (LSTM) is adopted. Although this method uses only proprioceptive sensors, its torque estimation capability is comparable to that of methods that use additional sensors. Then, the stiffness of a motion controller is adjusted based on the external torque in the stable region. Moreover, by adopting the Operational Space Formulation considering joint elasticity for a motion controller, high position tracking performance can be achieved with only proprioceptive sensors. The performance of the proposed method was validated through comparative experiments with two degrees of freedom (DoF) manipulator.

Maintaining balance to external pushes is one of the most important features for a humanoid to walk in a real environment. In particular, methods for counteracting to pushes using the centroidal angular momentum (CAM) control have been actively developed. In this paper, a CAM control scheme based on hierarchical quadratic programming (HQP) is proposed. The scheme of the CAM control consists of CAM tracking control and initial pose return control, which is hierarchically operated based on HQP to ensure the priority of CAM tracking performance. The proposed method is implemented in a capture point (CP) feedback control framework. Through simulations and experiments, the proposed method demonstrated more stable balance control performance than the previous method when the humanoid is walking in the presence of external perturbation.

In this study, a mechanism that realizes a novel structural form of the harmonic reducer is introduced. Conventional robots often use various mechanical reducers owing to low torque and high-speed characteristics of electric motors. Among them, harmonic reducers are frequently used because of their compact size and backlash-free precision. The plate harmonic reducer which uses the same topological geometry and reducing mechanism as the conventional harmonic reducer is a novel type of strain gear that changes its shape to a plate form for axial deformation. It has unique differences in terms of axial thickness, torsional stiffness, and efficiency due to its morphological characteristics. This study introduces and analyzes the reducing principle of the plate harmonic reducer and describes the methodological solutions for realization. Finally, the theoretical performance improvement and operating feasibility of the plate harmonic reducer are analyzed using finite element method and a 3D-printed prototype model.

As human-robot collaboration increases and robots are applied to everyday life, interest in safety issues is increasing. To be safely used in real life, in addition to collision prevention algorithms, robots need to quickly detect unexpected collisions and take appropriate actions. Recently, deep learning-based collision detection algorithms have been proposed to overcome the limitations of model-based collision detection methods, but there are also limitations on deep learning methods, especially in data collection. The collected data are often insufficient because collecting collision data is laborious and intrinsically imbalanced, meaning that the collision data are much smaller than the free-motion data. Moreover, since collecting collision data is risky and might cause potential damage to the robot, applying the deep learning method to a new target robot on mass production is highly restricted. Therefore, in this article, an inductive bias is imposed on network structure and input variable to be sample-efficiently trained, which is suitable for insufficient imbalanced data. The proposed modularized neural network removes the connection between other joints at the front part of the network, reducing the search space of the learnable parameter. Moreover, the input variable is selected to take both dynamics features and error-related features into account. Consequently, the proposed method is versatile, showing successful generalization performance to random motion, random collision location, and various loads at the end-effector. Furthermore, to circumvent the limitations of applying deep learning methods to mass production, transfer learning is proposed, which does not require any collision data from the target robot. The proposed data-mixture method and collision ratio adjustment method for fine-tuning are validated with two source robots and one target robot. The transferred network is effective to be applied on mass production without losing performance compared to a specific-robot-trained network(a network trained with specific robot data and applied to the same robot).

Reactivity to unforeseen disturbances is one of the most crucial characteristics for biped robots to walk robustly in the real world. Nevertheless, conventional walking methods generally have limited capability for generating rapid reactions to disturbances, because in these methods it is necessary to wait until the end of the preplanned time period to proceed to the next phase. In this study, to improve reactivity, we develop an event-based finite-state machine (E-FSM) for walking pattern generation. Reactivity is enhanced by determining the state transition conditions of the E-FSM only with time-independent events based on the present robot state. Moreover, in the E-FSM, the robot can walk robustly even when the center of mass and the swing foot motion are disturbed, by employing the capture point concept combined with a new swing foot position constraint. Finally, we propose to control the walking robot by incorporating the E-FSM with an inverse dynamics-based motion/force controller to achieve compliant behavior. This can provide safe responses to external disturbances. The developed method is verified by experiments on a 12-degrees-of-freedom torque-controlled biped robot while it locomotes under irregular external disturbances applied to the upper body or swing leg.

The actuator bandwidth limitation deteriorates the stability and performance of torque-based robot controllers. Operational space control is especially prone to this problem, since the limited bandwidth of a single actuator can reduce the performance of all related tasks simultaneously. In this article, an intuitive way to penalize low performance actuators is proposed to improve the performance of the operational space controller. The basic concept is to add joint torques only to high performance actuators, when the control bandwidth cannot reach the target level using all actuators. If that is not enough, additional torques are commanded to even higher performance actuators. This procedure can be executed recursively, meaning the controller can achieve almost maximum performance under the actuator bandwidth limitation. The proposed method was experimentally verified using the robot manipulator Franka Emika Panda.

As robots begin to collaborate with people in real life, safety needs to be rigorously ensured to reliably employ robots nearby. In addition to collision prevention algorithms, studies are being actively conducted on collision handling methods. Momentum Observer (MOB) was developed to estimate disturbance torque without using joint acceleration. However, the estimated disturbance from MOB contains not only the applied external torque but also model uncertainty such as friction and modeling error due to imprecise system identification. Our proposed method handles this problem by learning the model uncertainty with Long Short-Term Memory (LSTM) and thereby estimates the purely applied external torque with only proprioceptive sensors. The proposed method can be applied even when the information on the robot model is not available. The experiments using a real robot show that the external torque can be estimated and collisions can be detected accordingly even in a limited situation where a precise dynamics model and friction model are not available.

This paper proposes a novel and practical approach to enhance the computational efficiency of the hierarchical quadratic programming (HQP)-based whole-body control. The HQP method is known to offer control solutions satisfying strict priority with various constraints for multiple-tasks execution. However, it inherently comes at the price of high computation time to solve QP optimization problems in each hierarchical level which limits practicability in a real-time control system with fast sampling time. To mitigate this issue, we propose that the operational space formulation is incorporated into the HQP method, where the decision variables are intuitively defined at the task level and possess smaller dimensions. Indeed, it serves faster whole-body control solution for multiple tasks under equality and inequality constraints yet strictly fulfilling the task priority. The performance of the pro-posed method is experimentally verified on the actual floating-based humanoid, named TOCABI with 33 degrees-of-freedom. In addition, computation time is analyzed by comparison with conventional HQP and other advanced implementation forms.

This paper proposes a 6 degree of freedom (DoF) manipulator for haptic application. The proposed haptic device, named GHap, is designed based on the four-bar-linkage mechanism for linear motion with the ring-type gimbal mechanism. To improve the force display ability, the device is designed to compensate the gravity force of the manipulator by its own weight. The conceptual mechanical design is compared by placing the third joint, which controls the four-bar mechanism, in two different configurations. The forward kinematics and the jacobian of GHap are presented. Finally, the gravity compensation method and open-loop force display performance of the proposed haptic device are validated by an experiment with the GHap prototype.

The compliant motion of humanoid robots is one of their most important characteristics for interacting with humans and various environments in the real world. During walking, compliant motion ensures stable contact between the foot and ground, but walking stability is degraded by position tracking performance and unknown disturbances. To address the issue of instability of humanoid robot walking with compliant motion control, this paper proposes a model for real-time walking pattern generation considering the motion control performance of a robot. The dynamic model of a robot with a motion controller is described as a second-order system approximating position tracking performance with a linear inverted pendulum model to determine the relationship between the zero-moment point and center of mass (CoM). The CoM trajectory is calculated using preview control based on the dynamics model and current state of the robot. Therefore, even if the robot has the low tracking performance due to compliant motion control, the walking stability can be ensured. The proposed method was implemented on our humanoid robot, DYROS-JET, and its performance was demonstrated through improved stability during walking.

Torque controlled robots have the capability of implementing compliant behavior with back-drivability. In practice, however, joint elasticity often prevents an accurate position tracking performance of a robot. In particular, humanoid robots are influenced more by elasticity because of a long kinematic chain between the feet and hands. In this paper, we present a new inverse dynamics based control approach for torque controlled humanoid robots with joint elasticity. When formulating the operational space control framework, feedback control consists of only motor-related parts with measured motor angle values, and the link dynamics is compensated by the feedforward terms. The experiment results of the proposed approach show a noticeable improvement in the position tracking performance in 6-DoF manipulator. Finally, the proposed method was applied to a torque controlled biped robot for walking. Both stiff motion control of the CoM and compliant motion control of the foot were simultaneously achieved, demonstrating the advantage of the torque controlled robot.

The papers in this special section examine robotic technologies of the fourth industrial revolution or Industry 4.0 that will impact manufacturing industries. The concept of the fourth industrial revolution has drawn attention throughout the world and many efforts to define the concept in diverse fields have continued. Generally, the concept can be summarized as the technology convergence through hyper-intelligence and hyper-connectivity. The core technologies providing the thrust of the fourth industrial revolution, especially in the industrial informatics field, are Internet of Things, robotics, virtual reality, and artificial intelligence. As one of the most critical characteristics of the fourth industrial revolution technology is that the boundary between cyber space and physical space becomes unclear, innovations in industry and business initiate through the fusion of these two spaces. It is the robotic system that plays the key role as a physical medium linking cyber and physical spaces and even changing the physical space through direct interactions. In this sense, robotic system should be recognized as a crucial platform in performing tasks in the cyber-physical space.

Actuator modules of humanoid robots have relatively higher joint elasticity than those of industrial robots. Such joint elasticity could lead to negative effects on both the tracking performance and stability for walking. Especially, unstable contact between the foot and ground caused by joint elasticity is a critical problem, as it decreases the stability of position-controlled humanoid robots. To address this problem, this paper introduces a novel control scheme for position-controlled humanoid robots by which we can obtain not only enhance compliance capability for unknown contact but also suppress the vibration caused by joint elasticity. To estimate the disturbance caused by external forces and modeling errors between the actual system and nominal system, a disturbance observer based estimator is designed at each joint. Furthermore, a linear feedback controller for the flexible joint model and a gravity compensator is considered to reduce vibration and deflection due to the joint elasticity. The proposed control scheme was implemented on our humanoid robot, DYROS-JET, and its performance was demonstrated by improved stability during dynamic walking and stepping on objects.

Reactivity to unexpected situations is one of the most important characteristics of walking for real world applications. In this study, we introduce a reactive biped robot walking method that reflects only the current state of the robot. Therefore, time plan and trajectory tracking control are not required for robot walking, and this enables reactive behavior to unexpected contact or disturbance. The walking algorithm is realized through a whole-body control algorithm based on the operational space control framework, that possesses the capability to command the required force for tasks and also implement compliant task behavior by adjusting corresponding task gains. The performance of the proposed method is verified by experiments with a 12-DoF torque controlled biped robot. Robust walking is demonstrated when the foot is stopped by an unexpected obstacle or when the lateral motion is unexpectedly blocked and released by a human.

Low back pain is one of the world's most serious health problems. Conservative methods are recommended for back pain, and stabilization exercise as one of these methods has been proven to be effective. The “big 3” exercises are proven stabilization exercises, and it is able to train most of the muscles associated with low back stability. However, they are hard to be performed by some patients because the big 3 require sufficient strength in the upper and lower limbs as well as in the low back to maintain their postures. In this paper, we propose a rehabilitation robot SERA (Stabilization Exercise Robot Assistance system) to compensate for this shortcoming, and we describe the robot's developments and experiments. The proposed robot is designed to achieve effects similar to the big 3. The robot also ensures active exercises by voluntary control of the exerciser and provides anti-gravity force generated by series elastic actuators (SEAs) in order to adjust the exercise load. Experiments using surface electromyograph (sEMG) sensors with three subjects show the results for these features.

Autonomous vehicles are actively being developed from ADAS(Advanced Driver Assistance Systems) toward fully autonomous vehicles. Motion planning is one of the most important key technologies for fully autonomous vehicles, especially when they are operated in constrained narrow space such as parking lot. In this the motion planning is challenging because it requires many changes in forward and reverse directions and adjustments of position and orientation. In this paper, an efficient motion planning algorithm is proposed based on Rapidly-exploring Random Trees (RRT) by specifying desired orientation during the tree expansion. A tangential vector space for desired orientation is used to model nonholonomic constraints of a vehicle and geometric constraints of obstacles. The proposed algorithm has been tested on various situations and its results demonstrated much faster performance compared to a nonholonomic RRT algorithm.

One interesting field of robotics technology is related to the entertainment industry. Performing a musical piece using a robot is a difficult task because music presents many features like melody, rhythm, tone, harmony and so on. Addressing these tasks with a robot is not trivial to implement. Most of approaches which related to this specific field lacks of quality to perform in front of human audience. Implementation of human-like motions can not be properly achieved with a conventional robot actuator. Consequently, we exploit a new type of actuator which simplifies the drawbacks of a conventional one. We used Variable Stiffness Actuator(VSA) instead of using conventional actuator. We can control position, force, and stiffness, simultaneously by using VSA. The most important novel feature is its controllable stiffness. When the stiffness of the actuator is changed, the characteristics of the actuator's response also changes. We implemented the specific stroke which is called “double stroke” using one of variable stiffness actuator. Although the double stroke is known as a special stroke which could be performed by human only, double stroke is successfully implemented by stiffness variation.

Humans have had a long history of fascination with building intelligent machines that depict themselves or move animatedly. This article explores the history of robotic creatures like Egyptian figurines, Greek mechanical inventions, 18th century ingenious automata, and modern kinetic mise-en-scène and robotic artworks. Several interactive robots showed the potential for emotional interaction between humans and machines. In the context of human-robot interaction, empathy with robots requires further discussions on the interaction design.

The Kinetic Xylophone is an interactive instrument, which plays music with motorized mallets by gestures from spectators. This instrument consists of fourteen metallic tubes, and reacts through embedded infra-red sensors with spectators. Those distance signals trigger the rotation of mallet attached to motors. Spectators can easily perform music with this installation by hand-waving gestures instead of grabbing mallets. Thus, this kinetic art work can also be performed by children, and persons with disabilities.

The task-based control framework is well established for its ability to generate complex behavior in versatile robots. When executing multiple complex tasks, continuous and stable transition among these tasks is one of the most important issues. In this paper, the problem of task transition is discussed to achieve continuous transitions between arbitrary tasks effectively. Instead of modifying the control laws, the design of intermediate desired values to be realized by existing controllers is proposed. The proposed approach can deal with arbitrary task sets, with or without priorities, for insertion and removal, and with priority rearrangement for hierarchical sets of tasks. The solution is generic and can be used for any type of transition. Two examples of uses include a time-driven transition to execute a given task schedule and a transition depending on the robot configuration to perform joint-limit avoidance behaviors. The performance of the algorithm is verified in simulations and on a physical robot.

As the capability of robots is getting improved, more various tasks are expected to be performed by the robots. Complex operation of the robots can be composed of many different tasks. These tasks are executed sequentially, simultaneously, or in a combined way of both. This paper discusses the transition issue among multiple tasks on how the transition can be effectively and smoothly achieved. The proposed approach is to compose intermediate desired values to smooth the transitions rather than to modify control laws. The approach can be practically used on robotic systems without modification on their specific control algorithms. In this paper, multi-points control and joint limit avoidance are performed as applications of the proposed approach.

This paper presents a new methodology for the analysis and control of internal forces and center-of-mass (CoM) behavior, which are produced during multicontact interactions between humanoid robots and the environment. The approach leverages the virtual-linkage model that provides a physical representation of the internal and CoM resultant forces with respect to reaction forces on the supporting surfaces. A grasp/contact matrix describing the complex interactions between contact forces and CoM behavior is developed. Based on this model, a new torque-based approach for the control of internal forces is suggested and illustrated on the Asimo humanoid robot. The new controller is integrated into the framework for whole-body-prioritized multitasking, thus enabling the unified control of CoM maneuvers, operational tasks, and internal-force behavior. The grasp/contact matrix is also proposed to analyze and plan internal force and CoM control policies that comply with frictional properties of the links in contact.

This paper presents a methodology for the modeling and control of internal forces and moments produced during multi-contact interactions between humanoid robots and the environment. The approach is based on the virtual linkage model which provides a physical representation of the internal forces and moments acting between the various contacts. The forces acting at the contacts are decomposed into internal and resulting forces and the latter are represented at the robot's center of mass. A grasp/contact matrix describing the complex interactions between contact forces and center of mass behavior is developed. Based on this model, a new torque-based approach for the control of internal forces is suggested and illustrated on the Asimo humanoid robot. The new controller is integrated into the framework for whole-body prioritized multitasking enabling the unified control of operational tasks, postures, and internal forces.

Joint position control is a dominant paradigm in industrial robots. While it has been successful in various industrial tasks, joint position control is severely limited in performing advanced robotic tasks, especially in unstructured dynamic environments. This paper presents the concept of torque-to-position transformer designed to allow the implementation of joint torque control techniques on joint position-controlled robots. Robot torque control is essentially accomplished by converting desired joint torques into instantaneous increments of joint position inputs. For each joint, the transformer is based on the knowledge of the joint position servo controller and the closed-loop frequency response of that joint. This transformer can be implemented as a software unit and applied to any conventional position-controlled robot so that torque command to the robot becomes available. This approach has been experimentally implemented on the Honda ASIMO robot arm. The paper presents the results of this implementation which demonstrate the effectiveness of this approach.

Today most robots interact with the surroundings only with their end-effectors. However there are many benefits to utilizing contact along the entire length of robot body and links especially for human-like robots. Existing control strategies for link contact require knowledge of the contact point. In an uncertain environment, locating link contact point is difficult for most robots as they do not possess skin capable of sensing. We propose a probabilistic approach to link contact estimation based on geometric considerations and compliant motions. Since for many robots, link geometry is also uncertain, we broaden our approach to simultaneously estimate link shape and environment contact. Our experimental results demonstrate that efficiency of control is significantly improved by link contact estimation

Recently, on the base of distributed macro-mini actuation approach (DM2), a new robotic manipulator with hybrid actuation, air muscles-DC motor, has been developed. Among existing actuators, the hybrid actuation employs air muscles because they represent an advantageous tradeoff of performance and safety, due to their power/weight ratio and inherent compliance. The air muscles, however, are limited in bandwidth and their behavior is highly nonlinear. In order to overcome these limitations, the paper presents a torque control strategy based on a pair of differentially connected force-controlled air muscles. This controller was implemented and evaluated on a single joint testbed, first by itself and then as macro component into the Macro-Mini control strategy.

This paper presents a framework for the dynamical formulation and control of humanoid systems. In this framework unactuated virtual joints are used to describe the humanoid's configuration with respect to the inertial frame. The dynamics of the system are then formulated in a general manner that considers arbitrary contact with the environment. A control structure is implemented for both motion and contact forces that accounts for under-actuation due to the virtual joints. A strategy is also implemented to address transitions between different contact states. Simulation results are presented that demonstrate this overall framework for many behaviors such as standing, walking, jumping, and hand manipulation with walking

This paper discusses robotic telemanipulation with Kalman active observers and online stiffness estimation. Operational space techniques, feedback linearization, discrete state space methods, augmented states, and stochastic design are used to control a robotic manipulator with a haptic device. Stiffness estimation only based on force data (measured, desired, and estimated forces) is proposed, avoiding explicit position information. Stability and robustness to stiffness errors are discussed, as well as real-time adaptation techniques. Telepresence is analyzed. Experiments show high performance in contact with soft and hard surfaces.

This paper presents a compliant motion control framework for multiple contacts distributed over multiple links. The one link multi-contact control approach implemented in our previous work has been extended to contacts over multiple links. Experimental results demonstrate three point contact control on two links of a PUMA560 manipulator. A robust force control design is implemented with a Kalman estimator and full state feedback method to compensate for the modeling errors of the manipulator and environment.

This paper discusses the design of a telemanipulation system for haptic telepresence using Kalman active observers (AOBs). A robotic manipulator is controlled by the human operator through a haptic device. Free space, contact and impact experiments are presented, highlighting the capabilities of compliant motion control with AOBs. Telepresence and stability are analyzed taking into account the control design, the system stiffness and a spring-damper-mass model of the human arm. Haptic manipulation experiments on soft and hard surfaces are presented.

The paper describes the formulation of multi-contact compliant motion control. It extends our previous work to non-rigid environments. The contact forces are controlled through active observers (AOB), based on the Kalman filter theory. Noise characteristics enter in the control design and are estimated on-line. Experimental results are provided.

The paper discusses compliant motion control using Active Observers (AOBs) applied in robotic manipulators. Stochastic estimation strategies for haptic manipulation are introduced. Stability and robustness analysis is made as a function of stiffness mismatches. Real time adaptation is discussed.