Reinforcement Learning Impedance Control of a Robotic Prosthesis to Coordinate With Human Intact Knee Motion

This study aims to demonstrate reinforcement learning tracking control for automatically configuring the impedance parameters of a robotic knee prosthesis. While our previous studies involving human subjects have focused on tuning the impedance control parameters to meet a fixed, subjectively prescribed target motion profile to enable continuous walking with human-in-the-loop, in this paper we develop a new tracking control solution for a robotic knee to mimic the motion of the intact knee. As such, we replaced the prescribed target knee motion by an automatically generated profile based on the intact knee. As the profile of the intact knee varies over time due to human adaptation, we are presented with a challenging tracking control problem in the context of classical control theory. By formulating the “echo control” of the robotic knee as a reinforcement learning problem, we provide a promising new tool for real-time tracking control design without explicitly representing the underlying dynamics using a mathematical model, which can be difficult to obtain for a human-robot system. Additionally, our results may inspire future studies and new robotic prosthesis impedance control designs that can potentially coordinate between the intact and the robotic limbs toward daily use of the robotic device.