Deep Reinforcement Learning for Robotic Assembly of Mixed Deformable and Rigid Objects

Jianlan Luo; Eugen Solowjow; Chengtao Wen; Juan Aparicio Ojea; Alice M. Agogino

IROS 2018poster113 citations

Deep Reinforcement Learning for Robotic Assembly of Mixed Deformable and Rigid Objects

Jianlan Luo, Eugen Solowjow, Chengtao Wen, Juan Aparicio Ojea, Alice M. Agogino

Abstract

Reinforcement learning for assembly tasks can yield powerful robot control algorithms for applications that are challenging or even impossible for “conventional” feedback control methods. Insertion of a rigid peg into a deformable hole of smaller diameter is such a task. In this contribution we solve this task with Deep Reinforcement Learning. Force-torque measurements from a robot arm wrist sensor are thereby incorporated two-fold; they are integrated into the policy learning process and they are exploited in an admittance controller that is coupled to the neural network. This enables robot learning of contact-rich assembly tasks without explicit joint torque control or passive mechanical compliance. We demonstrate our approach in experiments with an industrial robot.

BibTeX

@inproceedings{iros2018_deepreinforcemen,
  title = {Deep Reinforcement Learning for Robotic Assembly of Mixed Deformable and Rigid Objects},
  author = {Jianlan Luo and Eugen Solowjow and Chengtao Wen and Juan Aparicio Ojea and Alice M. Agogino},
  booktitle = {IROS 2018},
  year = {2018}
}