Data-Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control

Abstract

The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application toward continuous robotic tracking control, especially for high-dimensional robots. This article proposes a data-informed residual reinforcement learning (DR-RL)-based robotic tracking control scheme applicable to robots with high dimensionality. The proposed DR-RL methodology outperforms common RL methods regarding sample efficiency and scalability. Specifically, we first decouple the original robot into low-dimensional robotic subsystems; and further utilize one-step backward data to construct incremental subsystems that are equivalent model-free representations of the aforementioned decoupled robotic subsystems. The formulated incremental subsystems allow for parallel learning to relieve computation load and offer us mathematical descriptions of robotic movements for conducting theoretical analysis. Then, we apply DR-RL to learn the tracking control policy, a combination of incremental base policy and incremental residual policy, under a parallel learning architecture. The incremental residual policy uses the guidance from the incremental base policy as the learning initialization and further learns from interactions with environments to endow the tracking control policy with adaptability toward dynamically changing environments. Our proposed DR-RL-based tracking control scheme is developed with rigorous theoretical analysis of system stability and weight convergence. The effectiveness of our proposed method is validated numerically on a 7-DoF KUKA iiwa robot manipulator and experimentally on a 3-DoF robot manipulator that would fail for other counterpart RL methods.