用低成本手柄直接操控机械臂，高效收集灵巧操作数据
DexDirect: Direct Kinesthetic Arm Guidance for Efficient Dexterous Demonstration Collection
- 操作者用手柄直接拖动6自由度机械臂，单摄像头将另一只手动作映射到16关节灵巧手
- 相比纯视觉和姿态追踪方法，成功演示数提升17.2倍和3.2倍，成功率90%
- 降低认知负荷，适合非专业人员快速采集高质量灵巧操作数据
大规模收集灵巧操作示范仍是机器人学习的主要瓶颈。高保真接口通常需要昂贵硬件和复杂设置，而低配置替代方案往往控制精度低且增加操作者认知负担。本文提出DexDirect，一种直接的体感手臂引导系统，用于高效采集灵巧操作示范。操作者通过手柄直接拖动6自由度重力补偿机械臂，同时单个摄像头将操作者另一只手的动作重定向至16关节13自由度灵巧手。用户研究显示，DexDirect相较于纯视觉（AnyTeleop）和姿态追踪（TeleDex）基线，成功示范数量分别提升17.2倍和3.2倍。经调整的NASA-TLX评估表明，尽管体力负荷上升，但心理需求、努力程度和挫败感显著降低。基于DexDirect示范训练的扩散策略在方块抓取与放置任务中达到90%成功率。结果表明，结合直接体感引导与视觉手部重定向的系统，是一种低设置、可扩展的灵巧操作示范采集方案。
原文摘要 · Abstract (English)
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
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