Teaching the User By Learning From the User:Personalizing Movement Control in Physical Human-robot Interaction

This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.

Augmented Virtual Stiffness Rendering of a Cable-driven SEA for Human-Robot Interaction

Human-robot interaction(HRI) is fundamental for human-centered robotics, and has been attracting intensive research for more than a decade. The series elastic actuator(SEA) provides inherent compliance, safety and further benefits for HRI, but the introduced elastic element also brings control difficulties. In this paper, we address the stiffness rendering problem for a cable-driven SEA system, to achieve either low stiffness for good transparency or high stiffness bigger than the physical spring constant, and to assess the rendering accuracy with quantified metrics. By taking a velocity-sourced model of the motor, a cascaded velocity-torque-impedance control structure is established. To achieve high fidelity torque control, the 2-DOF(degree of freedom) stabilizing control method together with a compensator has been used to handle the competing requirements on tracking performance, noise and disturbance rejection,and energy optimization in the cable-driven SEA system. The conventional passivity requirement for HRI usually leads to a conservative design of the impedance controller, and the rendered stiffness cannot go higher than the physical spring constant. By adding a phase-lead compensator into the impedance controller,the stiffness rendering capability was augmented with guaranteed relaxed passivity. Extensive simulations and experiments have been performed, and the virtual stiffness has been rendered in the extended range of 0.1 to 2.0 times of the physical spring constant with guaranteed relaxed passivity for physical humanrobot interaction below 5 Hz. Quantified metrics also verified good rendering accuracy.

Physical human-robot interaction estimation based control scheme for a hydraulically actuated exoskeleton designed for power amplification

We proposed a lower extremity exoskeleton for power amplification that perceives intended human motion via humanexoskeleton interaction signals measured by biomedical or mechanical sensors, and estimates human gait trajectories to implement corresponding actions quickly and accurately. In this study, torque sensors mounted on the exoskeleton links are proposed for obtaining physical human-robot interaction（pHRI） torque information directly. A Kalman smoother is adopted for eliminating noise and smoothing the signal data. Simultaneously, the mapping from the pHRI torque to the human gait trajectory is defined. The mapping is derived from the real-time state of the robotic exoskeleton during movement. The walking phase is identified by the threshold approach using ground reaction force. Based on phase identification, the human gait can be estimated by applying the proposed algorithm, and then the gait is regarded as the reference input for the controller. A proportional-integral-derivative control strategy is constructed to drive the robotic exoskeleton to follow the human gait trajectory. Experiments were performed on a human subject who walked on the floor at a natural speed wearing the robotic exoskeleton. Experimental results show the effectiveness of the proposed strategy.

操作行为估计的人机交互控制实验

为提高物理人机交互系统的柔顺效果,采用引入径向基神经网络进行物理人机交互(pHRI)过程中操作行为估计,以有限时间稳定的非奇异终端滑模为基础,改进对数项以提高pHRI过程中跟踪误差在面上运动的收敛能力,并部署操作人员和触觉力反馈装置的交互控制实验,完成控制理论和机器人学综合创新拓展实训环节。实验表明,在pHRI过程中,基于操作行为估计的人机交互控制方法能够实现人机柔顺交互,并且保持0.3 mm的跟踪误差,所设计方法的具有有效性。

A variable structure passivity control method for elastic joint robots based on cascaded high-order state estimation

Passivity-based controllers are widely used to facilitate physical interaction between humans and elastic joint robots,as they enhance the stability of the interaction system.However,the joint position tracking performance can be limited by the structures of these controllers when the system is faced with uncertainties and rough high-order system state measurements(such as joint accelerations and jerks).This study presents a variable structure passivity(VSP)control method for joint position tracking of elastic joint robots,which combines the advantages of passive control and variable structure control.This method ensures the tracking error converges in a finite time,even when the system faces uncertainties.The method also preserves the passivity of the system.Moreover,a cascaded observer,called CHOSSO,is also proposed to accurately estimate high-order system states,relying only on position and velocity signals.This observer allows independent implementation of disturbance compensation in the acceleration and jerk estimation channels.In particular,the observer has an enhanced ability to handle fast time-varying disturbances in physical human-robot interaction.The effectiveness of the proposed method is verified through simulations and experiments on a lower limb rehabilitation robot equipped with elastic joints.

An EMG-Based Biomimetic Variable Stiffness Modulation Strategy for Bilateral Motor Skills Relearning of Upper Limb Elbow Joint Rehabilitation

Bilateral rehabilitation systems with bilateral or unilateral assistive robots have been developed for hemiplegia patients to recover their one-side paralysis.However,the compliant robotic assistance to promote bilateral inter-limb coordination remains a challenge that should be addressed.In this paper,a biomimetic variable stiffness modulation strategy for the Variable Stiffness Actuator(VSA)integrated robotic is proposed to improve bilateral limb coordination and promote bilateral motor skills relearning.An Electromyography(EMG)-driven synergy reference stiffness estimation model of the upper limb elbow joint is developed to reproduce the muscle synergy effect on the affected side limb by independent real-time stiffness control.Additionally,the bilateral impedance control is incorporated for realizing compliant patient-robot interaction.Preliminary experiments were carried out to evaluate the tracking performance and investigate the multiple task intensities’influence on bilateral motor skills relearning.Experimental results evidence the proposed method could enable bilateral motor task skills relearning with wide-range task intensities and further promote bilateral inter-limb coordination.

Design and Analysis of a 2-DOF Actuator with Variable Stiffness Based on Leaf Springs

Variable Stiffness Actuator(VSA)is the core mechanism to achieve physical human–robot interaction,which is an inevitable development trend in robotic.The existing variable stiffness actuators are basically single degree-of-freedom(DOF)rotating joints,which are achieving multi-DOF motion by cascades and resulting in complex robot body structures.In this paper,an integrated 2-DOF actuator with variable stiffness is proposed,which could be used for bionic wrist joints or shoulder joints.The 2-DOF motion is coupling in one universal joint,which is different from the way of single DOF actuators cascade.Based on the 2-DOF orthogonal motion generated by the spherical wrist parallel mechanism,the stiffness could be adjusted by varying the effective length of the springs,which is uniformly distributed in the variable stiffness unit.The variable stiffness principle,the model design,and theoretical analysis of the VSA are discussed in this work.The independence of adjusting the equilibrium position and stiffness of the actuator is validated by experiments.The results show that the measured actuator characteristics are sufficiently matched the theoretical values.In the future,VSA could be used in biped robot or robotic arm,ensuring the safety of human–robot interaction.