Robot Reinforcement Learning on the Constraint Manifold
Puze Liu, Davide Tateo, Haitham Bou Ammar, Jan Peters
Abstract
Reinforcement learning in robotics is extremely challenging due to many practical issues, including safety, mechanical constraints, and wear and tear. Typically, these issues are not considered in the machine learning literature. One crucial problem in applying reinforcement learning in the real world is Safe Exploration, which requires physical and safety constraints satisfaction throughout the learning process. To explore in such a safety-critical environment, leveraging known information such as robot models and constraints is beneficial to provide more robust safety guarantees. Exploiting this knowledge, we propose a novel method to learn robotics tasks in simulation efficiently while satisfying the constraints during the learning process.
BibTeX
@inproceedings{
liu2021robot,
title={Robot Reinforcement Learning on the Constraint Manifold},
author={Puze Liu and Davide Tateo and Haitham Bou Ammar and Jan Peters},
booktitle={5th Annual Conference on Robot Learning },
year={2021},
url={https://openreview.net/forum?id=zwo1-MdMl1P}
}