A Data-Driven Reinforcement Learning Solution Framework for Optimal and Adaptive Personalization of a Hip Exoskeleton

TL;DR

This paper presents a novel data-driven reinforcement learning framework for personalized, adaptive control of a hip exoskeleton, improving human mobility assistance without prior human-robot dynamic modeling.

Contribution

It introduces a reinforcement learning-based personalization method for hip exoskeletons that adapts assistive torque profiles in real-time, enhancing human-robot coordination.

Findings

Successful validation on a human subject for unilateral hip extension

Reduced muscle activation levels with the RL controller

Feasibility of adaptive RL for assistive torque tuning

Abstract

Robotic exoskeletons are exciting technologies for augmenting human mobility. However, designing such a device for seamless integration with the human user and to assist human movement still is a major challenge. This paper aims at developing a novel data-driven solution framework based on reinforcement learning (RL), without first modeling the human-robot dynamics, to provide optimal and adaptive personalized torque assistance for reducing human efforts during walking. Our automatic personalization solution framework includes the assistive torque profile with two control timing parameters (peak and offset timings), the least square policy iteration (LSPI) for learning the parameter tuning policy, and a cost function based on transferred work ratio. The proposed controller was successfully validated on a healthy human subject to assist unilateral hip extension in walking. The results showed that the optimal and adaptive RL controller as a new approach was feasible for tuning assistive torque profile of the hip exoskeleton that coordinated with human actions and reduced activation level of hip extensor muscle in human.