DexTouch: 触覚で物体を探り操作する器用なマニピュレーション
DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexterity
視覚に頼らず触覚のみで暗所のランダムな物体を探索・操作する多指ロボットシステムを提案し、シミュレーションでの強化学習を実機に転移して触覚の重要性を示した。
著者: Kang-Won Lee, Yuzhe Qin, Xiaolong Wang, Soo-Chul Lim
分類: cs.RO, cs.LG
原文アブストラクト
The sense of touch is an essential ability for skillfully performing a variety of tasks, providing the capacity to search and manipulate objects without relying on visual information. In this paper, we introduce a multi-finger robot system designed to manipulate objects using the sense of touch, without relying on vision. For tasks that mimic daily life, the robot uses its sense of touch to manipulate randomly placed objects in dark. The objective of this study is to enable robots to perform blind manipulation by using tactile sensation to compensate for the information gap caused by the absence of vision, given the presence of prior information. Training the policy through reinforcement learning in simulation and transferring the trained policy to the real environment, we demonstrate that blind manipulation can be applied to robots without vision. In addition, the experiments showcase the importance of tactile sensing in the blind manipulation tasks. Our project page is available at https://lee-kangwon.github.io/dextouch/
