強化学習による股関節外骨格の最適・適応的パーソナライズ
A Data-Driven Reinforcement Learning Solution Framework for Optimal and Adaptive Personalization of a Hip Exoskeleton
人間-ロボット動力学のモデル化なしに強化学習で歩行時の股関節外骨格のアシストトルクを最適かつ適応的に個人調整する枠組みを提案し、健常者で有効性を検証した。
著者: Xikai Tu, Minhan Li, Ming Liu, Jennie Si, He, Huang
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
