Improved Reinforcement Learning Coordinated Control of a Mobile Manipulator using Joint Clamping

TL;DR

This paper enhances reinforcement learning for mobile manipulators, enabling better coordination and collision avoidance through joint penalties, resulting in higher success rates in complex, goal-oriented tasks.

Contribution

It introduces improved RL techniques for whole-body control of mobile manipulators, addressing joint penalties and limits for better coordination and obstacle avoidance.

Findings

Higher success rates in goal-reaching tasks

Ability to solve complex environments requiring coordination

Improved collision avoidance performance

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.