Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
This work addresses inefficiencies in mobile manipulation for robotics, though it appears incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of whole-body control for mobile manipulators by proposing an end-to-end reinforcement learning approach, achieving faster mission times compared to a state-of-the-art sampling-based method in simulation and validating it in challenging narrow corridor environments.
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.