Learning Stable Normalizing-Flow Control for Robotic Manipulation
This work addresses the need for stable control in robotic manipulation tasks, offering a novel integration of control theory into deep RL, though it is incremental in building upon existing RL frameworks.
The paper tackled the problem of ensuring control stability in reinforcement learning for robotic manipulation by introducing a normalizing-flow control structure that integrates with deep RL algorithms, achieving provable stability and improved exploration efficiency with reduced state space coverage and actuation efforts.
Reinforcement Learning (RL) of robotic manipulation skills, despite its impressive successes, stands to benefit from incorporating domain knowledge from control theory. One of the most important properties that is of interest is control stability. Ideally, one would like to achieve stability guarantees while staying within the framework of state-of-the-art deep RL algorithms. Such a solution does not exist in general, especially one that scales to complex manipulation tasks. We contribute towards closing this gap by introducing $\textit{normalizing-flow}$ control structure, that can be deployed in any latest deep RL algorithms. While stable exploration is not guaranteed, our method is designed to ultimately produce deterministic controllers with provable stability. In addition to demonstrating our method on challenging contact-rich manipulation tasks, we also show that it is possible to achieve considerable exploration efficiency--reduced state space coverage and actuation efforts--without losing learning efficiency.