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: this https URL

通过与基于传统工程方法的专业工业集成器的比较，研究表明基于深度强化学习的工业装配方法不仅可以超越已有的工业装配方法，还能超越人类运动系统，并且数据说明还有巨大的优化空间。