Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
Abstract
Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a reduction of workspace size. Even though different methods have been proposed to solve Whole-Body Control (WBC) online, they are either limited by a kinematic model or do not allow for reactive, online obstacle avoidance. In order to overcome these drawbacks, in this work, we propose an end-to-end Reinforcement Learning (RL) approach to WBC. We compared our learned controller against a state-of-the-art sampling-based method in simulation and achieved faster overall mission times. In addition, we validated the learned policy on our mobile manipulator RoyalPanda in challenging narrow corridor environments.
- Publication:
-
arXiv e-prints
- Pub Date:
- February 2020
- DOI:
- 10.48550/arXiv.2003.02637
- arXiv:
- arXiv:2003.02637
- Bibcode:
- 2020arXiv200302637K
- Keywords:
-
- Computer Science - Robotics;
- Computer Science - Machine Learning
- E-Print:
- 6 pages, 6 figures, 2 tables, submitted to RA-L/IROS