Tele-Catch：适应性远程操作用于灵巧动态3D物体捕捉
Tele-Catch: Adaptive Teleoperation for Dexterous Dynamic 3D Object Catching
AI总结：
本文提出Tele-Catch框架，通过融合手套信号与扩散策略去噪过程，实现动态物体捕捉任务中的共享自主控制，提升准确性和鲁棒性。
摘要：
远程操作是将人类灵巧性转移到机器人中的关键范式，但大多数现有工作针对初始静止物体，如抓取或 manipulation。动态物体捕捉，其中物体在接触前移动，仍被忽视。纯远程操作在此任务中常因时间、姿态和力误差而失败，凸显了需要结合人类输入与自主策略的共享自主性。为此，我们提出了Tele-Catch，一种系统框架，用于动态物体捕捉的灵巧手远程操作。其核心是设计DAIM，一种动态感知的自适应整合机制，通过将手套基远程操作信号融合到扩散策略去噪过程中，实现共享自主性。它根据交互物体状态自适应调节控制。为提高策略鲁棒性，我们引入DP-U3R，将点云观测的无监督几何表示整合到扩散策略学习中，实现几何感知决策。大量实验表明，Tele-Catch在动态捕捉任务中显著提高了准确性和鲁棒性，同时在不同灵巧手实现和之前未见的物体类别中也表现出一致的提升。
英文摘要：
Teleoperation is a key paradigm for transferring human dexterity to robots, yet most prior work targets objects that are initially static, such as grasping or manipulation. Dynamic object catch, where objects move before contact, remains underexplored. Pure teleoperation in this task often fails due to timing, pose, and force errors, highlighting the need for shared autonomy that combines human input with autonomous policies. To this end, we present Tele-Catch, a systematic framework for dexterous hand teleoperation in dynamic object catching. At its core, we design DAIM, a dynamics-aware adaptive integration mechanism that realizes shared autonomy by fusing glove-based teleoperation signals into the diffusion policy denoising process. It adaptively modulates control based on the interaction object state. To improve policy robustness, we introduce DP-U3R, which integrates unsupervised geometric representations from point cloud observations into diffusion policy learning, enabling geometry-aware decision making. Extensive experiments demonstrate that Tele-Catch significantly improves accuracy and robustness in dynamic catching tasks, while also exhibiting consistent gains across distinct dexterous hand embodiments and previously unseen object categories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
