Deep reinforcement learning for high precision assembly tasks
Tadanobu Inoue, Giovanni De Magistris, Asim Munawar, Tsuyoshi Yokoya, Ryuki Tachibana
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.
BibTeX
@inproceedings{iros2017_deepreinforcemen,
title = {Deep reinforcement learning for high precision assembly tasks},
author = {Tadanobu Inoue and Giovanni De Magistris and Asim Munawar and Tsuyoshi Yokoya and Ryuki Tachibana},
booktitle = {IROS 2017},
year = {2017}
}