Robotic Knee Parameter Tuning Using Approximate Policy Iteration

This paper presents an online model-free reinforcement learning based controller realized by approximate dynamic programming for a robotic knee as part of a human-machine system. Traditionally, prosthesis wearers’ gait performance is improved by manually tuning the impedance parameters. In this paper, we show that the parameter tuning problem can be formulated as an optimal control problem and thus solved by dynamic programming. Toward this goal, we constructed an quadratic instantaneous cost, which resulted in a value function that could be approximated by a neural network. The control policy is then solved by the least-squared method iteratively, a framework of which we refer to as approximate policy iteration. We performed extensive simulations based on prosthetic kinetics and human performance data extracted from real human subjects. Our results show that the proposed parameter tuning algorithm can be readily used for adaptive optimal tuning of prosthetic knee control parameters and the tuning process is time and sample efficient.