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

• URL: http://arxiv.org/abs/2003.02637v1
• Date: Tue, 25 Feb 2020 21:21:57 GMT
• Title: Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
• Authors: Julien Kindle, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Roland Siegwart and Juan Nieto
• Abstract summary: We propose an end-to-end Reinforcement Learning (RL) approach to Whole-Body Control (WBC) We compared our learned controller against a state-of-the-art sampling-based method in simulation and achieved faster overall mission times.
• Score: 31.150823782805283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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