Robust Multi-Modal Policies For Industrial Assembly Via Reinforcement Learning And Demonstrations: A Large-Scale Study

Over the past several years there has been a considerable research investment into learning-based approaches for tasks inspired by industrial manufacturing, but despite significant progress, these techniques have yet to be adopted by in the real-world. We argue that it is the prohibitively large design space for Deep Reinforcement Learning (DRL), rather than algorithmic limitations per se, that are truly responsible for this lack of adoption. Pushing these techniques into the industrial main-stream requires a paradigm which differs significantly from the academic mindset. In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against results of a professional industrial integrator on the recently established NIST assembly benchmark. We explain the design choices, representing several years of investigation, which enabled our DRL system to consistently outperform the integrator's baseline in terms of both speed and reliability. Finally, we conclude with a competition between our DRL system and a human on a challenge task of insertion into a randomly moving target. This study suggests that DRL is capable of outperforming not only established engineered approaches, but the human motor system as well, and that there remains significant room for improvement. Videos can be found on our project website:https://sites.google.com/view/shield-nist.