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Mobile manipulation is usually achieved by sequentially executing base andmanipulator movements. This simplification, however, leads to a loss inefficiency and in some cases a reduction of workspace size. Even thoughdifferent 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. Wecompared our learned controller against a state-of-the-art sampling-basedmethod in simulation and achieved faster overall mission times. In addition, wevalidated the learned policy on our mobile manipulator RoyalPanda inchallenging narrow corridor environments.

Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning