VRテレオペレーションによる複雑な操作タスクの深層模倣学習
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
民生用VRヘッドセットとハンドトラッキングでロボットを直感的に遠隔操作し、そのデモからピクセル入力→行動出力の深層模倣学習ポリシーを獲得する手法を提案した。
著者: Tianhao Zhang, Zoe McCarthy, Owen Jow, Dennis Lee, Xi Chen, Ken Goldberg, Pieter Abbeel
分類: cs.LG, cs.RO
原文アブストラクト
Imitation learning is a powerful paradigm for robot skill acquisition. However, obtaining demonstrations suitable for learning a policy that maps from raw pixels to actions can be challenging. In this paper we describe how consumer-grade Virtual Reality headsets and hand tracking hardware can be used to naturally teleoperate robots to perform complex tasks. We also describe how imitation learning can learn deep neural network policies (mapping from pixels to actions) that can acquire the demonstrated skills. Our experiments showcase the effectiveness of our approach for learning visuomotor skills.
