目標補助付きActor-Criticによる点群を用いた6Dロボット把持
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
物体の点群を入力に6D把持の閉ループ制御方策を学習する手法を提案し、模倣学習と強化学習を組み合わせた目標補助付きActor-Criticで未見物体への把持性能を向上させた。
著者: Lirui Wang, Yu Xiang, Wei Yang, Arsalan Mousavian, Dieter Fox
分類: cs.RO, cs.CV
原文アブストラクト
6D robotic grasping beyond top-down bin-picking scenarios is a challenging task. Previous solutions based on 6D grasp synthesis with robot motion planning usually operate in an open-loop setting, which are sensitive to grasp synthesis errors. In this work, we propose a new method for learning closed-loop control policies for 6D grasping. Our policy takes a segmented point cloud of an object from an egocentric camera as input, and outputs continuous 6D control actions of the robot gripper for grasping the object. We combine imitation learning and reinforcement learning and introduce a goal-auxiliary actor-critic algorithm for policy learning. We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover system to improve the grasping performance of unseen objects. Our videos and code can be found at https://sites.google.com/view/gaddpg .
