Improved Reinforcement Learning Coordinated Control of a Mobile Manipulator using Joint Clamping
arXiv:2110.01926
Abstract
Many robotic path planning problems are continuous, stochastic, and high-dimensional. The ability of a mobile manipulator to coordinate its base and manipulator in order to control its whole-body online is particularly challenging when self and environment collision avoidance is required. Reinforcement Learning techniques have the potential to solve such problems through their ability to generalise over environments. We study joint penalties and joint limits of a state-of-the-art mobile manipulator whole-body controller that uses LIDAR sensing for obstacle collision avoidance. We propose directions to improve the reinforcement learning method. Our agent achieves significantly higher success rates than the baseline in a goal-reaching environment and it can solve environments that require coordinated whole-body control which the baseline fails.
arXiv comment6 pages, 6 figures, 3 tables
References in corpus (7)
- Learning agile and dynamic motor skills for legged robots
- Solving Rubik's Cube with a Robot Hand
- Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
- A Distributional Perspective on Reinforcement Learning
- Whole-Body MPC for a Dynamically Stable Mobile Manipulator
- Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
- Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning