全身制御の深層学習：マニピュレーションと移動を統合したポリシー学習
Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion
脚式ロボットの腕と脚を別々に制御せず、強化学習で全身を統合したポリシーを学習し、低コストな脚式マニピュレータで動的で敏捷な動作を実現した。
著者: Zipeng Fu, Xuxin Cheng, Deepak Pathak
分類: cs.RO, cs.AI, cs.CV, cs.LG, cs.SY, eess.SY
原文アブストラクト
An attached arm can significantly increase the applicability of legged robots to several mobile manipulation tasks that are not possible for the wheeled or tracked counterparts. The standard hierarchical control pipeline for such legged manipulators is to decouple the controller into that of manipulation and locomotion. However, this is ineffective. It requires immense engineering to support coordination between the arm and legs, and error can propagate across modules causing non-smooth unnatural motions. It is also biological implausible given evidence for strong motor synergies across limbs. In this work, we propose to learn a unified policy for whole-body control of a legged manipulator using reinforcement learning. We propose Regularized Online Adaptation to bridge the Sim2Real gap for high-DoF control, and Advantage Mixing exploiting the causal dependency in the action space to overcome local minima during training the whole-body system. We also present a simple design for a low-cost legged manipulator, and find that our unified policy can demonstrate dynamic and agile behaviors across several task setups. Videos are at https://maniploco.github.io
