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Automatically configuring a robotic prosthesis to fit its user's needs andphysical conditions is a great technical challenge and a roadblock to theadoption of the technology. Previously, we have successfully developedreinforcement learning (RL) solutions toward addressing this issue. Yet, ourdesigns were based on using a subjectively prescribed target motion profile forthe robotic knee during level ground walking. This is not realistic fordifferent users and for different locomotion tasks. In this study for the firsttime, we investigated the feasibility of RL enabled automatic configuration ofimpedance parameter settings for a robotic knee to mimic the intact knee motionin a co-adapting environment. We successfully achieved such tracking control byan online policy iteration. We demonstrated our results in both OpenSimsimulations and two able-bodied (AB) subjects.

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