6 accepted papers
Wearable lower limb robots are promising technologies to assist human locomotion. Soft robotic exosuits introduce a promising solution for reducing muscle effort and metabolic cost as they are lightweight, transparent and inherently safe. However, it is challenging to effectively control such soft r
The feasibility of gait entrainment to periodic mechanical perturbations varies with perturbation magnitude in neurotypical individuals. Effective design of gait entrainment studies thus requires a systematic approach to personalize periodic perturbation parameters. However, current studies still re
This study presents an innovative reinforcement learning (RL) control approach to facilitate soft exosuit-assisted human walking. Our goal is to address the ongoing challenges in developing reliable RL-based methods for controlling physical devices. To overcome key obstacles—such as limited data, th…
We introduce a new stage reward estimator named the long $N$-step surrogate stage (LNSS) reward for deep reinforcement learning (RL). It aims at mitigating the high variance problem, which has shown impeding successful convergence of learning, hurting task performance, and hindering applications of…
This is the first attempt at considering human influence in the reinforcement learning control of a robotic lower limb prosthesis toward symmetrical walking in real world situations. We propose a collaborative multi-agent reinforcement learning (cMARL) solution framework for this highly complex and…
Quantitatively characterizing a locomotion performance objective for a human-robot system is an important consideration in the assistive wearable robot design towards human-robot symbiosis. This problem, however, has only been addressed sparsely in the literature. In this study, we propose a new inv