触覚強化学習による未知物体のSim2Realマニピュレーション
Sim2Real Manipulation on Unknown Objects with Tactile-based Reinforcement Learning
多様な物体をシミュレータ上で触覚入力を用いた強化学習により訓練し、未知物体への汎化と実機転移を実現する手法を提案。
著者: Entong Su, Chengzhe Jia, Yuzhe Qin, Wenxuan Zhou, Annabella Macaluso, Binghao Huang, Xiaolong Wang
分類: cs.RO
原文アブストラクト
Using tactile sensors for manipulation remains one of the most challenging problems in robotics. At the heart of these challenges is generalization: How can we train a tactile-based policy that can manipulate unseen and diverse objects? In this paper, we propose to perform Reinforcement Learning with only visual tactile sensing inputs on diverse objects in a physical simulator. By training with diverse objects in simulation, it enables the policy to generalize to unseen objects. However, leveraging simulation introduces the Sim2Real transfer problem. To mitigate this problem, we study different tactile representations and evaluate how each affects real-robot manipulation results after transfer. We conduct our experiments on diverse real-world objects and show significant improvements over baselines for the pivoting task. Our project page is available at https://tactilerl.github.io/.
