Improved Reinforcement Learning Coordinated Control of a Mobile Manipulator using Joint Clamping
This work addresses the problem of coordinated control for mobile manipulators in robotics, but it appears incremental as it builds on an existing state-of-the-art controller with specific enhancements.
The paper tackled the challenge of online whole-body control for a mobile manipulator requiring collision avoidance, and the proposed reinforcement learning improvements achieved significantly higher success rates in goal-reaching environments and solved tasks that the baseline method failed.
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.