Safe and Individualized Motion Planning for Upper-limb Exoskeleton Robots Using Human Demonstration and Interactive Learning

TL;DR

This paper introduces a motion planning approach for upper-limb exoskeletons that combines human demonstration and interactive learning to ensure safe, personalized assistance tailored to individual patients' needs.

Contribution

It presents a novel scheme that integrates probabilistic movement primitives with interactive learning for individualized, safe motion planning in exoskeleton robots.

Findings

Successful generation of personalized trajectories from healthy subjects.

Effective adaptation of trajectories based on patient feedback.

Validation of the control scheme through experimental results.

Abstract

A typical application of upper-limb exoskeleton robots is deployment in rehabilitation training, helping patients to regain manipulative abilities. However, as the patient is not always capable of following the robot, safety issues may arise during the training. Due to the bias in different patients, an individualized scheme is also important to ensure that the robot suits the specific conditions (e.g., movement habits) of a patient, hence guaranteeing effectiveness. To fulfill this requirement, this paper proposes a new motion planning scheme for upper-limb exoskeleton robots, which drives the robot to provide customized, safe, and individualized assistance using both human demonstration and interactive learning. Specifically, the robot first learns from a group of healthy subjects to generate a reference motion trajectory via probabilistic movement primitives (ProMP). It then learns from the patient during the training process to further shape the trajectory inside a moving safe region. The interactive data is fed back into the ProMP iteratively to enhance the individualized features for as long as the training process continues. The robot tracks the individualized trajectory under a variable impedance model to realize the assistance. Finally, the experimental results are presented in this paper to validate the proposed control scheme.