Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study

Abstract

Over the past several years there has been a considerable research investment into learning-based approaches to industrial assembly, but despite significant progress these techniques have yet to be adopted by industry. 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 mainstream requires an industry-oriented 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 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 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.

arXiv commentRSS 2021

References in corpus (13)

• Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
• Conservative Q-Learning for Offline Reinforcement Learning
• Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
• Distributed Prioritized Experience Replay
• Hindsight Experience Replay
• Deep Q-learning from Demonstrations
• Challenges of Real-World Reinforcement Learning
• Self-Imitation Learning
• Reinforcement and Imitation Learning for Diverse Visuomotor Skills
• AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
• Scaling data-driven robotics with reward sketching and batch reinforcement learning
• COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
• DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction