DexDirect: 直接運動感覚アームガイダンスによる効率的な巧緻操作デモ収集
DexDirect: Direct Kinesthetic Arm Guidance for Efficient Dexterous Demonstration Collection
重力補償されたロボットアームを直接操作し、ウェブカメラで手の動きをロボットハンドにリターゲットすることで、低コストかつ高効率に巧緻操作のデモを収集する手法を提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Beom Jun Kim, Shiu-Jen Wang, Jonathan Liu, Alvin Zhu, Quanyou Wang, Hanzhang Fang, Feng Xu, Mingzhang Zhu, Yuchen Cui, Dennis W. Hong
分類: cs.RO
原文アブストラクト
Scalable collection of dexterous manipulation demonstrations remains a major bottleneck for robot learning. High-fidelity interfaces often require costly hardware and extensive setup, while low-setup, low cost alternatives tend to provide less precise control and impose greater cognitive workload on operators. We present DexDirect, a direct kinesthetic arm guidance for efficient dexterous demonstration collection. The operator drags a 6-DoF gravity-compensated robot arm directly by a handle, while a single webcam retargets operator's other hand onto a 16 joints 13-DoF dexterous robot hand. User studies suggest DexDirect collects 17.2x and 3.2x more successful demonstrations compared to purely vision (AnyTeleop) and pose-tracking (TeleDex) baselines. An adapted NASA-TLX shows DexDirect greatly reduces mental demand, effort, and frustration, despite raising physical demand. A diffusion policy trained on DexDirect demonstrations reaches a 90% success rate on a cube pick-and-place task. These results suggest that direct kinesthetic arm guidance combined with vision-based hand retargeting provides an efficient low-setup and scalable interface for collecting dexterous manipulation demonstrations
