Showing papers on "Robot kinematics published in 2016"

Overtrust of Robots in Emergency Evacuation Scenarios

Abstract: Robots have the potential to save lives in emergency scenarios, but could have an equally disastrous effect if participants overtrust them. To explore this concept, we performed an experiment where a participant interacts with a robot in a non-emergency task to experience its behavior and then chooses whether to follow the robot's instructions in an emergency or not. Artificial smoke and fire alarms were used to add a sense of urgency. To our surprise, all 26 participants followed the robot in the emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before. We performed additional exploratory studies investigating different failure modes. Even when the robot pointed to a dark room with no discernible exit the majority of people did not choose to safely exit the way they entered.

Tracking Control of Networked Multi-Agent Systems Under New Characterizations of Impulses and Its Applications in Robotic Systems

Abstract: This paper examines the problem of tracking control of networked multi-agent systems with multiple delays and impulsive effects, whose results are applied to mechanical robotic systems. Four kinds of impulsive effects are taken into account: 1) both the strengths of impulsive effects and the number of nodes injected with impulses are time dependent; 2) the strengths of impulsive effects occur according to certain probabilities and the number of nodes under impulsive control is time varying; 3) the strengths of impulses are time varying, whereas the number of nodes with impulses takes place according to certain probabilities; 4) both the strengths of impulses and the number of nodes with impulsive control occur according to certain probabilities. By utilizing the comparison principle, criteria are established for these different cases and a relationship between the frequencies (occurrence probabilities) of impulses and systems' parameters is unveiled. Finally, an example for tracking control of robotic systems is provided to show the effectiveness of the presented results.

Leader–Follower Formation and Tracking Control of Mobile Robots Along Straight Paths

Abstract: We address the problem of tracking control of multiple mobile robots advancing in formation along straight-line paths. We use a leader–follower approach, and hence, we assume that only one swarm leader robot has the information of the reference trajectory. Then, each robot receives information from one intermediary leader only. Therefore, the communications graph forms a simple spanning directed tree. As the existence of a spanning tree is necessary to achieve consensus, it is the minimal configuration possible to achieve the formation-tracking objective. From a technological viewpoint, this has a direct impact on the simplicity of its implementation; e.g., less sensors are needed. Our controllers are partially linear time-varying with a simple added nonlinearity satisfying a property of persistency of excitation, tailored for nonlinear systems. Structurally speaking, the controllers are designed with the aim of separating the tasks of position-tracking and orientation. Our main results ensure the uniform global asymptotic stabilization of the closed-loop system, and hence, they imply robustness with respect to perturbations. All these aspects make our approach highly attractive in diverse application domains of vehicles’ formations such as factory settings.

Anticipatory Robot Control for Efficient Human-Robot Collaboration

Abstract: Efficient collaboration requires collaborators to monitor the behaviors of their partners, make inferences about their task intent, and plan their own actions accordingly. To work seamlessly and efficiently with their human counterparts, robots must similarly rely on predictions of their users' intent in planning their actions. In this paper, we present an anticipatory control method that enables robots to proactively perform task actions based on anticipated actions of their human partners. We implemented this method into a robot system that monitored its user's gaze, predicted his or her task intent based on observed gaze patterns, and performed anticipatory task actions accord- ing to its predictions. Results from a human-robot interaction experiment showed that anticipatory control enabled the robot to respond to user requests and complete the task faster-2.5 seconds on average and up to 3.4 seconds-compared to a robot using a reactive control method that did not anticipate user intent. Our findings highlight the promise of performing anticipatory actions for achieving efficient human-robot teamwork.

Optimized Assistive Human–Robot Interaction Using Reinforcement Learning

Abstract: An intelligent human–robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human–robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot’s dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an ${x}$ - ${y}$ table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.

Automatic 3-D Manipulation of Soft Objects by Robotic Arms With an Adaptive Deformation Model

Abstract: In this paper, we present a new feedback method to automatically servo-control the 3-D shape of soft objects with robotic manipulators. The soft object manipulation problem has recently received a great deal of attention from robotics researchers because of its potential applications in, e.g., food industry, home robots, medical robotics, and manufacturing. A major complication to automatically control the shape of an object is the estimation of its deformation properties, which determines how the manipulator's motion actively transforms into deformations. Note that these properties are rarely known beforehand, and its offline parametric identification is difficult and/or impractical to conduct in many applications. To cope with this issue, we developed a new algorithm that computes in real time the unknown deformation parameters of a soft object; this algorithm provides a valuable adaptive behavior to the deformation controller, something we cannot achieve with traditional fixed-model approaches. In contrast with most controllers in the literature, our new method can explicitly servo-control 3-D deformations (and not just 2-D image projections) in an entirely model-free way. To validate the proposed adaptive controller, we present a detailed experimental study with robotic manipulators.

Vision-Based Model Predictive Control for Steering of a Nonholonomic Mobile Robot

Abstract: In this paper, we have developed a novel visual servo-based model predictive control method to steer a wheeled mobile robot (WMR) moving in a polar coordinate toward a desired target. The proposed control scheme has been realized at both kinematics and dynamics levels. The kinematics predictive steering controller generates command of desired velocities that are achieved by employing a low-level motion controller, while the dynamics predictive controller directly generates torques used to steer the WMR to the target. In the presence of both kinematics and dynamics constraints, the control design is carried out using quadratic programming (QP) for optimal performance. The neurodynamic optimization technique, particularly the primal-dual neural network, is employed to solve the QP problems. Theoretical analysis has been first performed to show that the desired velocities can be achieved with the guaranteed stability, as well as with the global convergence to the optimal solutions of formulated convex programming problems. Experiments have then been carried out to validate the effectiveness of the proposed control scheme and illustrate its advantage over the conventional methods.

Formation Control of Leader–Follower Mobile Robots’ Systems Using Model Predictive Control Based on Neural-Dynamic Optimization

Abstract: In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for controlling leader–follower mobile robots formation. Consider obstacles in the environments, a control strategy is proposed for the formations which includes separation-bearing-orientation scheme (SBOS) for regular leader–follower formation and separation-distance scheme (SDS) for obstacle avoidance. During the formation motion, the leader robot shall track a desired trajectory and the desire leader–follower relationship can be maintained through SBOS method; meanwhile, the followers can avoid the collision by applying the SDS. The formation-error kinematics of both SBOS and SDS are derived and a constrained quadratic programming (QP) can be obtained by transforming the MPC method. Then, over a finite-receding horizon, the QP problem can be solved by utilizing the primal-dual neural network (PDNN) with parallel capability. The computation complexity can be greatly reduced by the implemented neural-dynamic optimization. Compared with other existing formation control approaches, the developed solution in this paper is rooted in NMPC techniques with input constraints and the novel QP problem formulation. Finally, experimental studies of the proposed formation control approach have been performed on several mobile robots to verify the effectiveness.

Vision-Based Distributed Formation Control Without an External Positioning System

Abstract: In this paper, we present a fully distributed solution to drive a team of robots to reach a desired formation in the absence of an external positioning system that localizes them. Our solution addresses two fundamental problems that appear in this context. First, we propose a 3-D distributed control law, designed at a kinematic level, that uses two simultaneous consensus controllers: one to control the relative orientations between robots, and another for the relative positions. The convergence to the desired configuration is shown by comparing the system with time-varying orientations against the equivalent approach with fixed orientations, showing that their difference vanishes as time goes to infinity. Second, in order to apply this controller to a group of aerial robots, we combine this idea with a novel sensor fusion algorithm to estimate the relative pose of the robots by using onboard cameras and information from the inertial measurement unit. The algorithm removes the influence of roll and pitch from the camera images and estimates the relative pose between robots by using a structure from the motion approach. Simulation results, as well as hardware experiments with a team of three quadrotors, demonstrate the effectiveness of the controller and the vision system working together.

Vision-Guided Aerial Manipulation Using a Multirotor With a Robotic Arm

Abstract: This paper presents a vision guidance approach using an image-based visual servo (IBVS) for an aerial manipulator combining a multirotor with a multidegree of freedom robotic arm. To take into account the dynamic characteristics of the combined manipulation platform, the kinematic and dynamic models of the combined system are derived. Based on the combined model, a passivity-based adaptive controller which can be applied on both position and velocity control is designed. The position control is utilized for waypoint tracking such as taking off and landing, and the velocity control is engaged when the platform is guided by visual information. In addition, a guidance law utilizing IBVS is employed with modifications. To secure the view of an object with an eye-in-hand camera, IBVS is utilized with images taken from a fisheye camera. Also, to compensate underactuation of the multirotor, an image adjustment method is developed. With the proposed control and guidance laws, autonomous flight experiments involving grabbing and transporting an object are carried out. Successful experimental results demonstrate that the proposed approaches can be applied in various types of manipulation missions.

Practice Makes Perfect: An Optimization-Based Approach to Controlling Agile Motions for a Quadruped Robot

Abstract: This article approaches the problem of controlling quadrupedal running and jumping motions with a parameterized, model-based, state-feedback controller. Inspired by the motor learning principles observed in nature, our method automatically fine tunes the parameters of our controller by repeatedly executing slight variations of the same motion task. This learn-through-practice process is performed in simulation to best exploit computational resources and to prevent the robot from damaging itself. To ensure that the simulation results match the behavior of the hardware platform, we introduce and validate an accurate model of the compliant actuation system. The proposed method is experimentally verified on the torque-controllable quadruped robot StarlETH by executing squat jumps and dynamic gaits, such as a running trot, pronk, and a bounding gait.

3D dynamic walking on stepping stones with control barrier functions

Abstract: 3D dynamical walking subject to precise footstep placements is crucial for navigating real world terrain with discrete footholds. We present a novel methodology that combines control Lyapunov functions—to achieve periodic walking—and control Barrier functions—to enforce strict constraints on step length and step width—unified in a single optimization-based controller. We numerically validate our proposed method by demonstrating dynamic 3D walking at 0.6 m/s on DURUS, a 23 degree-of-freedom underactuated humanoid robot.

Kinematic and dynamic modelling of UR5 manipulator

Abstract: UR robotic arms are from a series of lightweight, fast, easy to program, flexible, and safe robotic arms with 6 degrees of freedom. The fairly open control structure and low level programming access with high control bandwidth have made them of interest for many researchers. This paper presents a complete set of mathematical kinematic and dynamic, Matlab, and Simmechanics models for the UR5 robot. The accuracy of the developed mathematical models are demonstrated through kinematic and dynamic analysis. The Simmechanics model is developed based on these models to provide high quality visualisation of this robot for simulation of it in Matlab environment. The models are developed for public access and readily usable in Matlab environment. A position control system has been developed to demonstrate the use of the models and for cross validation purpose.

Visual Servoing of Quadrotors for Perching by Hanging From Cylindrical Objects

Abstract: This letter addresses vision-based localization and servoing for quadrotors to enable autonomous perching by hanging from cylindrical structures using only a monocular camera. We focus on the problems of relative pose estimation, control, and trajectory planning for maneuvering a robot relative to cylinders with unknown orientations. We first develop a geometric model that describes the pose of the robot relative to a cylinder. Then, we derive the dynamics of the system, expressed in terms of the image features. Based on the dynamics, we present a controller, which guarantees asymptotic convergence to the desired image space coordinates. Finally, we develop an effective method to plan dynamically feasible trajectories in the image space, and we provide experimental results to demonstrate the proposed method under different operating conditions such as hovering, trajectory tracking, and perching.

Coordination of Multiple Robotic Fish With Applications to Underwater Robot Competition

Abstract: This paper is concerned with the coordination control of multiple biomimetic robotic fish in highly dynamic aquatic environments by building a hybrid centralized system. With the aid of the results of biorobotics and control techniques, a radio-controlled multijoint robotic fish and its locomotion control are developed. To enable a closed control loop, a visual subsystem that is responsible for tracking of multiple moving objects is constructed and implemented in real time. Furthermore, a behavior-based hierarchical architecture in conjunction with fuzzy reinforcement learning is proposed to accomplish effective coordination among multiple swimming robots. Finally, experiments on 2vs2 water polo game are carried out to verify the proposed coordination control scheme. Over the past eight years, this multirobot platform has been successfully applied to international underwater robot competitions to promote innovative research and education in underwater robotics.

Projecting robot intentions into human environments

Abstract: Trained human co-workers can often easily predict each other's intentions based on prior experience. When collaborating with a robot coworker, however, intentions are hard or impossible to infer. This difficulty of mental introspection makes human-robot collaboration challenging and can lead to dangerous misunderstandings. In this paper, we present a novel, object-aware projection technique that allows robots to visualize task information and intentions on physical objects in the environment. The approach uses modern object tracking methods in order to display information at specific spatial locations taking into account the pose and shape of surrounding objects. As a result, a human co-worker can be informed in a timely manner about the safety of the workspace, the site of next robot manipulation tasks, and next subtasks to perform. A preliminary usability study compares the approach to collaboration approaches based on monitors and printed text. The study indicates that, on average, the user effectiveness and satisfaction is higher with the projection based approach.

Buzz: An extensible programming language for heterogeneous swarm robotics

Abstract: We present Buzz, a novel programming language for heterogeneous robot swarms. Buzz advocates a compositional approach, offering primitives to define swarm behaviors both from the perspective of the single robot and of the overall swarm. Single-robot primitives include robot-specific instructions and manipulation of neighborhood data. Swarm-based primitives allow for the dynamic management of robot teams, and for sharing information globally across the swarm. Self-organization stems from the completely decentralized mechanisms upon which the Buzz run-time platform is based. The language can be extended to add new primitives (thus supporting heterogeneous robot swarms), and its run-time platform is designed to be laid on top of other frameworks, such as the Robot Operating System. We showcase the capabilities of Buzz by providing code examples, and analyze scalability and robustness of the run-time platform through realistic simulated experiments with representative swarm algorithms.

Markerless Human–Manipulator Interface Using Leap Motion With Interval Kalman Filter and Improved Particle Filter

Abstract: The aim of this paper is to propose a novel markerless human–robot interface, which is derived from the idea that the manipulator copies the movements of human hands. With this method, one operator could control dual robots through both his or her hands in a contactless and markerless environment. In order to obtain the position and orientation of human hands in real time, a sensor called leap motion (LM) is employed in this paper. However, because of the tracking errors and noises of the sensor, the measurement errors increase with time. Therefore, interval Kalman filter (IKF) and improved particle filter (IPF) are used to estimate the position and the orientation of the human hands, respectively. Furthermore, in order to avoid the perceptive limitations and the motor limitations, which prevent the operator from carrying out the high-precision experiment, a modification of adaptive multispace transformation (AMT) method is raised to assist the operator to determine the posture of the manipulator. The greatest strength of our method is that it is totally contactless and could estimate the pose of the human hands accurately and stably without any assistance from markers. A series of experiments have been conducted to verify the human–manipulator interface system, and the results show that the system is indeed of high availability and fault tolerance in teleoperation, which means even a novice can easily and successfully control robots with this human–manipulator interface.

Multi-objective compositions for collision-free connectivity maintenance in teams of mobile robots

Abstract: Compositional barrier functions are proposed in this paper to systematically compose multiple objectives for teams of mobile robots. The objectives are first encoded as barrier functions, and then composed using AND and OR logical operators. The advantage of this approach is that compositional barrier functions can provably guarantee the simultaneous satisfaction of all composed objectives. The compositional barrier functions are applied to the example of ensuring collision avoidance and static/dynamical graph connectivity of teams of mobile robots. The resulting composite safety and connectivity barrier certificates are verified experimentally on a team of four mobile robots.

Stability analysis and design of momentum-based controllers for humanoid robots

Abstract: Envisioned applications for humanoid robots call for the design of balancing and walking controllers. While promising results have been recently achieved, robust and reliable controllers are still a challenge for the control community dealing with humanoid robotics. Momentum-based strategies have proven their effectiveness for controlling humanoids balancing, but the stability analysis of these controllers is still missing. The contribution of this paper is twofold. First, we numerically show that the application of state-of-the-art momentum-based control strategies may lead to unstable zero dynamics. Secondly, we propose simple modifications to the control architecture that avoid instabilities at the zero-dynamics level. Asymptotic stability of the closed loop system is shown by means of a Lyapunov analysis on the linearized system's joint space. The theoretical results are validated with both simulations and experiments on the iCub humanoid robot.

Neural Network-Based Control of Networked Trilateral Teleoperation With Geometrically Unknown Constraints

Abstract: Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master–single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force–motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach.

Robot reading human gaze: Why eye tracking is better than head tracking for human-robot collaboration

Abstract: Robots are at the position to become our everyday companions in the near future. Still, many hurdles need to be cleared to achieve this goal. One of them is the fact that robots are still not able to perceive some important communication cues naturally used by humans, e.g. gaze. In the recent past, eye gaze in robot perception was substituted by its proxy, head orientation. Such an approach is still adopted in many applications today. In this paper we introduce performance improvements to an eye tracking system we previously developed and use it to explore if this approximation is appropriate. More precisely, we compare the impact of the use of eye- or head-based gaze estimation in a human robot interaction experiment with the iCub robot and naive subjects. We find that the possibility to exploit the richer information carried by eye gaze has a significant impact on the interaction. As a result, our eye tracking system allows for a more efficient human-robot collaboration than a comparable head tracking approach, according to both quantitative measures and subjective evaluation by the human participants.

Initiative in Robot Assistance during Collaborative Task Execution

Abstract: Collaborative robots are quickly gaining momentum in real-world settings. This has motivated many new research questions in human-robot collaboration. In this paper, we address the questions of whether and when a robot should take initiative during joint human-robot task execution. We develop a system capable of autonomously tracking and performing table-top object manipulation tasks with humans and we implement three different initiative models to trigger robot actions. Human initiated help gives control of robot action timing to the user; robot-initiated reactive help triggers robot assistance when it detects that the user needs help; and robot-initiated proactive help makes the robot help whenever it can. We performed a user study (N=18) to compare these trigger mechanisms in terms of task performance, usage characteristics, and subjective preference. We found that people collaborate best with a proactive robot, yielding better team fluency and high subjective ratings. However, they prefer having control of when the robot should help, rather than working with a reactive robot that only helps when it is needed.

Innovation in Underwater Robots: Biologically Inspired Swimming Snake Robots

Abstract: Increasing efficiency by improving locomotion methods is a key issue for underwater robots. Moreover, a number of different control design challenges must be solved to realize operational swimming robots for underwater tasks. This article proposes and experimentally validates a straightline-path-following controller for biologically inspired swimming snake robots. In particular, a line-of-sight (LOS) guidance law is presented, which is combined with a sinusoidal gait pattern and a directional controller that steers the robot toward and along the desired path. The performance of the path-following controller is investigated through experiments with a physical underwater snake robot for both lateral undulation and eel-like motion. In addition, fluid parameter identification is performed, and simulation results based on the identified fluid coefficients are presented to obtain a back-to-back comparison with the motion of the physical robot during the experiments. The experimental results show that the proposed control strategy successfully steers the robot toward and along the desired path for both lateral undulation and eel-like motion patterns.

Maneuvering Control of Planar Snake Robots Using Virtual Holonomic Constraints

Abstract: This paper investigates the problem of maneuvering control for planar snake robots. The control objective is to make the center of mass of the snake robot converge to a desired path and traverse the path with a desired velocity. The proposed feedback control strategy enforces virtual constraints encoding a lateral undulatory gait, parametrized by the states of dynamic compensators used to regulate the orientation and forward speed of the snake robot.

Speeding-Up Robot Exploration by Exploiting Background Information

Abstract: The ability to autonomously learn a model of an environment is an important capability of a mobile robot. In this paper, we investigate the problem of exploring a scene given background information in form of a topo-metric graph of the environment. Our method is relevant for several real-world applications in which the rough structure of the environment is known beforehand. We present an approach that exploits such background information and enables a robot to cover the environment with its sensors faster compared to a greedy exploration system without this information. We implemented our exploration system in ROS and evaluated it in different environments. As the experimental results demonstrate, our proposed method significantly reduces the overall trajectory length needed to cover the environment with the robot’s sensors and thus yields a more efficient exploration strategy compared to state-of-the-art greedy exploration, if the additional information is available.

Fall Detection and Prevention Control Using Walking-Aid Cane Robot

Abstract: An intelligent walking-aid cane robot is developed for assisting the elderly and the physically challenged with walking. A motion control method is proposed for the cane robot based on human walking intention estimation. Moreover, the safety is investigated for both the cane robot and the elderly. The fall detection and prevention concepts are proposed to guarantee the safety of the elderly while walking with the cane robot. However, the deficiency of the cane robot is that it can be overturned easily because of its small size and light weight. Therefore, a controllable universal joint is designed for adjusting the tilted angle of its stick. The stability of the cane robot during the fall prevention procedure can then be enhanced by controlling the tilted angle of stick to an optimal position. A center of pressure (COP)-based fall detection (COP-FD) method is used to detect the risk of falling. In this method, the user's COP is calculated in real time using an integrated force sensory system, which comprises a six-axis force/torque sensor and an inshoe load sensor. When the COP reaches the boundary of the specified safety area, i.e., the support polygon, it is assessed that the user is going to fall down. The COP-FD method can be used in various cases of falling. However, for cases of stumbling, a rapid fall detection method is proposed based on leg motion detection, and Dubois' fuzzy possibility theory is applied to adapt to different users. When the risk of falling has been detected, a fall prevention impedance control is executed considering both the interaction compliance and system stability. In the study, a control simulation platform was established to obtain the optimal controller parameters, and all the proposed methods were finally verified through simulations and experiments.

Pipe Network Locomotion with a Snake Robot

Abstract: We present a method of achieving whole-body compliant motions with a snake robot that allows the robot to automatically adapt to the shape of its environment. This feature is important to pipe navigation because it allows the robot to adapt to changes in diameter and junctions, even though the robot lacks mechanical compliance or tactile sensing. Rather than reasoning in the configuration space of robot joint angles, the compliant controller estimates the overall state of the robot in terms of the parameters of a low-dimensional control function, i.e., a gait. The controller then commands new gait parameters relative to that estimated state. Performing closed-loop control in this lower-dimensional parameter space, rather than the robot's full configuration space, exploits the intuitive connection between the gait parameters and higher-level robot behavior. Furthermore, the ability to automatically adjust gait parameters with this controller enables more sophisticated motions that would previously have been too complex to be controlled manually.

Assistive Control System for Upper Limb Rehabilitation Robot

Abstract: This paper presents an assistive control system with a special kinematic structure of an upper limb rehabilitation robot embedded with force/torque sensors. A dynamic human model integrated with sensing torque is used to simulate human interaction under three rehabilitation modes: active mode, assistive mode, and passive mode. The hereby proposed rehabilitation robot, called NTUH-ARM, provides 7 degree-of- freedom (DOF) motion and runs subject to an inherent mapping between the 7 DOFs of the robot arm and the 4 DOFs of the human arm. The Lyapunov theory is used to analyze the stability of the proposed controller design. Clinical trials have been conducted with six patients, one of which acts as a control. The results of these experiments are positive and STREAM assessment by physical therapists also reveals promising results.