強化学習による移動マニピュレータの全身制御
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
移動マニピュレータの全身制御をエンドツーエンド強化学習で実現し、シミュレーションで従来手法より速いタスク遂行と狭い廊下での実機検証を示した論文。
著者: Julien Kindle, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Roland Siegwart, Juan Nieto
分類: cs.RO, cs.LG
原文アブストラクト
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.
