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Over the past several years there has been a considerable research investmentinto learning-based approaches to industrial assembly, but despite significantprogress these techniques have yet to be adopted by industry. We argue that itis the prohibitively large design space for Deep Reinforcement Learning (DRL),rather than algorithmic limitations per se, that are truly responsible for thislack of adoption. Pushing these techniques into the industrial mainstreamrequires an industry-oriented paradigm which differs significantly from theacademic mindset. In this paper we define criteria for industry-oriented DRL,and perform a thorough comparison according to these criteria of one family oflearning approaches, DRL from demonstration, against a professional industrialintegrator on the recently established NIST assembly benchmark. We explain thedesign choices, representing several years of investigation, which enabled ourDRL system to consistently outperform the integrator baseline in terms of bothspeed and reliability. Finally, we conclude with a competition between our DRLsystem and a human on a challenge task of insertion into a randomly movingtarget. This study suggests that DRL is capable of outperforming not onlyestablished engineered approaches, but the human motor system as well, and thatthere remains significant room for improvement. Videos can be found on ourproject website: https://sites.google.com/view/shield-nist.

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