Robotic learning from demonstration of therapist's time-varying assistance to a patient in trajectory-following tasks

Impedance Variation and Learning Strategies in Human-Robot Interaction

In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed.

Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation

Introduction Occupational rehabilitation often involves functional capacity evaluations (FCE) that use simulated work tasks to assess work ability. Currently, there exists no single, streamlined solution to simulate all or a large number of standard work tasks. Such a system would improve FCE and functional rehabilitation through simulating reaching maneuvers and more dexterous functional tasks that are typical of workplace activities. This paper reviews efforts to develop robotic FCE solutions that incorporate machine learning algorithms. Methods We reviewed the literature regarding rehabilitation robotics, with an emphasis on novel techniques incorporating robotics and machine learning into FCE. Results Rehabilitation robotics aims to improve the assessment and rehabilitation of injured workers by providing methods for easily simulating workplace tasks using intelligent robotic systems. Machine learning-based approaches combine the benefits of robotic systems with the expertise and experience of human therapists. These innovations have the potential to improve the quantification of function as well as learn the haptic interactions provided by therapists to assist patients during assessment and rehabilitation. This is done by allowing a robot to learn based on a therapist's motions ("demonstrations") what the desired workplace activity ("task") is and how to recreate it for a worker with an injury ("patient"). Through Telerehabilitation and internet connectivity, these robotic assessment techniques can be used over a distance to reach rural and remote locations. Conclusions While the research is in the early stages, robotics with integrated machine learning algorithms have great potential for improving traditional FCE practice.

Towards Data-Driven Autonomous Robot-Assisted Physical Rehabilitation Therapy

Task-oriented therapy consists of three stages: demonstration, observation and assistance. While demonstration using robots has been extensively studied, the other two stages rarely involve robots. This paper focuses on the transition between observation and assistance. More specifically, we tackle the robot's decision making problem of whether to assist a patient or not based on the observation. The proposed method is to train a discrete tunnel shape 3-D decision boundary through correct demonstration to classify motions. Additional conditions such as slow progress, self correction and overshot motions are taken into account of the decision making. Preliminary experiments have been performed on BAXTER robot for a cup reaching task. The BAXTER robot is programmed to react according to the decision boundary. It assists the patient when the patient's hand position is determined by the proposed algorithm to be unacceptable. Multiple cases including correct motion, continuous assistance, overshot, misaim and slow progress are tested. Results have confirmed the feasibility of the proposed method, which can reduce the current shortage of physical rehabilitation therapists.