強化学習によるロボット膝義足の自動インピーダンス制御：共適応環境下で健常膝の動きを模倣する
Reinforcement Learning Enabled Automatic Impedance Control of a Robotic Knee Prosthesis to Mimic the Intact Knee Motion in a Co-Adapting Environment
強化学習を用いてロボット膝義足のインピーダンスパラメータを自動調整し、共適応環境下で健常膝の動きを模倣する追従制御を実現した。シミュレーションと健常者2名で有効性を示した。
著者: Ruofan Wu, Minhan Li, Zhikai Yao, Jennie Si, He, Huang
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Automatically configuring a robotic prosthesis to fit its user's needs and physical conditions is a great technical challenge and a roadblock to the adoption of the technology. Previously, we have successfully developed reinforcement learning (RL) solutions toward addressing this issue. Yet, our designs were based on using a subjectively prescribed target motion profile for the robotic knee during level ground walking. This is not realistic for different users and for different locomotion tasks. In this study for the first time, we investigated the feasibility of RL enabled automatic configuration of impedance parameter settings for a robotic knee to mimic the intact knee motion in a co-adapting environment. We successfully achieved such tracking control by an online policy iteration. We demonstrated our results in both OpenSim simulations and two able-bodied (AB) subjects.
