  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.
