Abstract-The high precision assembly of mechanical parts requires precision that exceeds that of robots. Conventional part-mating methods used in the current manufacturing require numerous parameters to be tediously tuned before deployment. We show how a robot can successfully perform a peg-in-hole task with a tight clearance through training a recurrent neural network with reinforcement learning. In addition to reducing manual effort, the proposed method also shows a better fitting performance with a tighter clearance and robustness against positional and angular errors for the peg-in-hole task. The neural network learns to take the optimal action by observing the sensors of a robot to estimate the system state. The advantages of our proposed method are validated experimentally on a 7-axis articulated robot arm.
Inoue et al. REF show how to use a recurrent network to learn search and insertion actions on a high precision assembly task.
Deep reinforcement learning for high precision assembly tasks
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Computer Science Engineering