发表机构
UCLA（加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出DexDirect方法，结合直接运动学手臂引导与视觉手部重定向，采集灵巧操作演示的效率更高、认知负荷更低，训练的扩散策略在立方体抓取任务上达到90%成功率。
AI 中文摘要
灵巧操作演示的可扩展采集仍是机器人学习的主要瓶颈。高保真接口常需昂贵硬件与大量设置，而低设置、低成本的替代方案往往控制精度较低，会给操作者带来更大的认知负荷。我们提出DexDirect，一种用于高效灵巧演示采集的直接运动学手臂引导方法。操作者通过手柄直接拖动6自由度（DoF）重力补偿机械臂，同时单台网络摄像头将操作者的另一只手重定向到16关节、13自由度的灵巧机器人手上。用户研究显示，与纯视觉方案AnyTeleop和姿态跟踪方案TeleDex的基线相比，DexDirect采集到的成功演示数量分别是前者的17.2倍和3.2倍。改编后的NASA任务负荷指数（NASA-TLX）表明，尽管DexDirect增加了物理需求，但它大幅降低了心理需求、努力程度和挫败感。基于DexDirect演示训练的扩散策略在立方体抓取和放置任务上达到了90%的成功率。这些结果表明，直接运动学手臂引导与基于视觉的手部重定向相结合，为采集灵巧操作演示提供了一种高效、低设置且可扩展的接口。
英文摘要
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
Comments8pages, 6 figures
