arXiv 2101.034872021-01-10cs.ROcs.SYeess.SY

Reinforcement Learning Enabled Automatic Impedance Control of a Robotic Knee Prosthesis to Mimic the Intact Knee Motion in a Co-Adapting Environment

Arizona State University（亚利桑那州立大学）；North Carolina State University（北卡罗来纳州立大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）

Ruofan Wu, Minhan Li, Zhikai Yao, Jennie Si, He, Huang

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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.

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