  A long-standing goal of the Human-Robot Collaboration (HRC) in manufacturing
systems is to increase the collaborative working efficiency. In line with the
trend of Industry 4.0 to build up the smart manufacturing system, the Co-robot
in the HRC system deserves better designing to be more self-organized and to
find the superhuman proficiency by self-learning. Inspired by the impressive
machine learning algorithms developed by Google Deep Mind like Alphago Zero, in
this paper, the human-robot collaborative assembly working process is formatted
into a chessboard and the selection of moves in the chessboard is used to
analogize the decision making by both human and robot in the HRC assembly
working process. To obtain the optimal policy of working sequence to maximize
the working efficiency, the robot is trained with a self-play algorithm based
on reinforcement learning, without guidance or domain knowledge beyond game
rules. A neural network is also trained to predict the distribution of the
priority of move selections and whether a working sequence is the one resulting
in the maximum of the HRC efficiency. An adjustable desk assembly is used to
demonstrate the proposed HRC assembly algorithm and its efficiency.
