Robotic insertion tasks are characterized by contact and friction mechanics, making them challenging for conventional feedback control methods due to unmodeled physical effects. Reinforcement learning (RL) is a promising approach for learning control policies in such settings. However, RL can be unsafe during exploration and might require a large amount of real-world training data, which is expensive to collect. In this paper, we study how to use meta-reinforcement learning to solve the bulk of the problem in simulation by solving a family of simulated industrial insertion tasks and then adapt policies quickly in the real world. We demonstrate our approach by training an agent to successfully perform challenging real-world insertion tasks using less than 20 trials of real-world experience. Videos and other material are available at https://pearl-insertion.github.io/
@article{arxiv.2004.14404,
title = {Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks},
author = {Gerrit Schoettler and Ashvin Nair and Juan Aparicio Ojea and Sergey Levine and Eugen Solowjow},
journal= {arXiv preprint arXiv:2004.14404},
year = {2020}
}