基于强化学习和演示的工业装配鲁棒多模态策略：大规模研究
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study
摘要：
在过去的几年里，已经有相当大的研究投资到基于学习的方法，以工业组装，但尽管取得了重大进展，这些技术尚未被采用的行业。我们认为，深度强化学习（DRL）的设计空间过大，而不是算法本身的限制，才是导致这种缺乏采用的真正原因。将这些技术推向工业主流需要一个与学术思维截然不同的面向工业的范式。在本文中，我们定义了面向行业的DRL的标准，并进行了彻底的比较，根据这些标准的一个家庭的学习方法，DRL从示范，对专业的工业集成商在最近建立的NIST组装基准。我们解释的设计选择，代表了几年的调查，使我们的日间行车灯系统，以一贯优于集成商的速度和可靠性方面的基线。最后，我们得出结论，我们的DRL系统和人类之间的竞争插入到一个随机移动的目标的挑战性任务。这项研究表明，DRL不仅能够超越已建立的工程方法，而且还能够超越人类运动系统，并且仍然有很大的改进空间。视频可以在我们的项目网站上找到：https://sites.google.com/view/shield-nist。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
