A Computational Approach for Human-like Motion Generation in Upper Limb Exoskeletons Supporting Scapulohumeral Rhythms

This paper proposes a computational approach for generation of reference path for upper-limb exoskeletons considering the scapulohumeral rhythms of the shoulder. The proposed method can be used in upper-limb exoskeletons with 3 Degrees of Freedom (DoF) in shoulder and 1 DoF in elbow, which are capable of supporting shoulder girdle. The developed computational method is based on Central Nervous System (CNS) governing rules. Existing computational reference generation methods are based on the assumption of fixed shoulder center during motions. This assumption can be considered valid for reaching movements with limited range of motion (RoM). However, most upper limb motions such as Activities of Daily Living (ADL) include large scale inward and outward reaching motions, during which the center of shoulder joint moves significantly. The proposed method generates the reference motion based on a simple model of human arm and a transformation can be used to map the developed motion for other exoskeleton with different kinematics. Comparison of the model outputs with experimental results of healthy subjects performing ADL, show that the proposed model is able to reproduce human-like motions.Comment: In 2017 IEEE International Symposium on Wearable & Rehabilitation Robotics (WeRob2017

Online augmentation of learned grasp sequence policies for more adaptable and data-efficient in-hand manipulation

When using a tool, the grasps used for picking it up, reposing, and holding it in a suitable pose for the desired task could be distinct. Therefore, a key challenge for autonomous in-hand tool manipulation is finding a sequence of grasps that facilitates every step of the tool use process while continuously maintaining force closure and stability. Due to the complexity of modeling the contact dynamics, reinforcement learning (RL) techniques can provide a solution in this continuous space subject to highly parameterized physical models. However, these techniques impose a trade-off in adaptability and data efficiency. At test time the tool properties, desired trajectory, and desired application forces could differ substantially from training scenarios. Adapting to this necessitates more data or computationally expensive online policy updates. In this work, we apply the principles of discrete dynamic programming (DP) to augment RL performance with domain knowledge. Specifically, we first design a computationally simple approximation of our environment. We then demonstrate in physical simulation that performing tree searches (i.e., lookaheads) and policy rollouts with this approximation can improve an RL-derived grasp sequence policy with minimal additional online computation. Additionally, we show that pretraining a deep RL network with the DP-derived solution to the discretized problem can speed up policy training.Comment: 7 pages (6+1 bibliography), 4 figures, 1 table, 2 algorithms, to appear in ICRA 202

Improving Human-Robot Interaction in Upper-Limb Rehabilitation Exoskeletons through Human-Like Path Generation and Patient-Cooperative Control

Exoskeleton-based therapy is a growing area and can provide benefits over conventional manual therapy due to the robots’ ability in providing high intensity and long therapy sessions, accurate measurements, and precise control over individual joints. Despite the recent progress in the development of upper-limb rehabilitation exoskeletons, their employment in rehabilitation settings is limited. Among various reasons, one main limiting factor is the problem of human-robot interaction in these systems, which hasn’t been studied thoroughly. From one perspective, improving human-robot interaction requires developing systems that conform to the user. The alignment of the robotic and human joint centers is the requirement for achieving kinematic compliance and ergonomic design. This problem is, however, challenging due to the complexity of the biological joints such as the shoulder. Improved interaction can also be achieved by using exoskeleton motion generation algorithms which can produce motions similar to the natural motions of the users. Additionally, developing control algorithms that better resemble the supervisory role of the therapist during manual rehabilitation, can provide a more compliant therapy procedure and will further expand the usability of exoskeletons in the rehabilitation settings. This work addresses the three mentioned areas of improvement by offering unique solutions in each area. First, to develop an ergonomic upper-limb exoskeleton that can correspond to the functionality of the human arm and move in accordance with the upper-limb joints, a kinematic design including a new inner shoulder design is introduced based on the systematic approaches of product development. The developed kinematic design is examined through kinematic and experimental analysis on the design. The novel design of this new upper-limb exoskeleton, called CLEVERarm, allows for accurate alignment in the shoulder joint by moving the device joints in harmony with the human body. Additionally, a new control framework is developed which enables automated and patient-cooperative rehabilitation. For this purpose, a new computational-based reference path generation method for upper-limb exoskeletons is introduced which generates human-like reference motions for the exoskeleton. The proposed method generates human-like motions in the configuration space of the human arm and transfers the generated motions to the configuration space of the exoskeleton. The accuracy of the introduced model is verified through experimental and simulation results. Additionally, a new performance-based compliant controller is developed based on variable admittance control. The developed controller updates the prescribed reference path to be followed, based on the user exerted forces, to comply with the user’s movement. However, deviations from the reference path are limited and incorrect posture is not permitted. The performance-based and assist-as-needed nature of the developed controller encourages patient contribution. Experimental verification results show the capability of the developed algorithm to be employed in upper-limb exoskeletons for the rehabilitation of patients with various levels of motor impairment

Improving Human-Robot Interaction in Upper-Limb Rehabilitation Exoskeletons through Human-Like Path Generation and Patient-Cooperative Control

Exoskeleton-based therapy is a growing area and can provide benefits over conventional manual therapy due to the robots’ ability in providing high intensity and long therapy sessions, accurate measurements, and precise control over individual joints. Despite the recent progress in the development of upper-limb rehabilitation exoskeletons, their employment in rehabilitation settings is limited. Among various reasons, one main limiting factor is the problem of human-robot interaction in these systems, which hasn’t been studied thoroughly. From one perspective, improving human-robot interaction requires developing systems that conform to the user. The alignment of the robotic and human joint centers is the requirement for achieving kinematic compliance and ergonomic design. This problem is, however, challenging due to the complexity of the biological joints such as the shoulder. Improved interaction can also be achieved by using exoskeleton motion generation algorithms which can produce motions similar to the natural motions of the users. Additionally, developing control algorithms that better resemble the supervisory role of the therapist during manual rehabilitation, can provide a more compliant therapy procedure and will further expand the usability of exoskeletons in the rehabilitation settings. This work addresses the three mentioned areas of improvement by offering unique solutions in each area. First, to develop an ergonomic upper-limb exoskeleton that can correspond to the functionality of the human arm and move in accordance with the upper-limb joints, a kinematic design including a new inner shoulder design is introduced based on the systematic approaches of product development. The developed kinematic design is examined through kinematic and experimental analysis on the design. The novel design of this new upper-limb exoskeleton, called CLEVERarm, allows for accurate alignment in the shoulder joint by moving the device joints in harmony with the human body. Additionally, a new control framework is developed which enables automated and patient-cooperative rehabilitation. For this purpose, a new computational-based reference path generation method for upper-limb exoskeletons is introduced which generates human-like reference motions for the exoskeleton. The proposed method generates human-like motions in the configuration space of the human arm and transfers the generated motions to the configuration space of the exoskeleton. The accuracy of the introduced model is verified through experimental and simulation results. Additionally, a new performance-based compliant controller is developed based on variable admittance control. The developed controller updates the prescribed reference path to be followed, based on the user exerted forces, to comply with the user’s movement. However, deviations from the reference path are limited and incorrect posture is not permitted. The performance-based and assist-as-needed nature of the developed controller encourages patient contribution. Experimental verification results show the capability of the developed algorithm to be employed in upper-limb exoskeletons for the rehabilitation of patients with various levels of motor impairment