CyberDemo: シミュレーション上の人間デモンストレーションを拡張する実世界巧みな操作
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation
シミュレーション内で人間のデモンストレーションを大規模にデータ拡張し、実世界の巧みな操作タスクへ転移する模倣学習手法を提案。未見の物体にも汎化し、実世界のデモより高い成功率を示した。
著者: Jun Wang, Yuzhe Qin, Kaiming Kuang, Yigit Korkmaz, Akhilan Gurumoorthy, Hao Su, Xiaolong Wang
分類: cs.RO, cs.CV
原文アブストラクト
We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentation in a simulated environment, CyberDemo outperforms traditional in-domain real-world demonstrations when transferred to the real world, handling diverse physical and visual conditions. Regardless of its affordability and convenience in data collection, CyberDemo outperforms baseline methods in terms of success rates across various tasks and exhibits generalizability with previously unseen objects. For example, it can rotate novel tetra-valve and penta-valve, despite human demonstrations only involving tri-valves. Our research demonstrates the significant potential of simulated human demonstrations for real-world dexterous manipulation tasks. More details can be found at https://cyber-demo.github.io
