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.

翻译：遥操作是将人类灵巧性迁移至机器人的关键范式，但现有研究多聚焦于初始静止的物体（如抓取或操作）。针对接触前存在运动的动态物体抓取任务，目前仍缺乏充分探索。纯遥操作在该任务中常因时序、姿态及力控制误差而失败，亟需融合人类输入与自主策略的共享控制机制。为此，我们提出Tele-Catch——面向动态物体抓取的灵巧手遥操作统一框架。其核心模块DAIM（动力学感知自适应集成机制）通过将数据手套的遥操作信号注入扩散策略去噪过程实现共享控制，并能根据交互物体状态自适应调节控制权重。为增强策略鲁棒性，我们提出DP-U3R，将点云观测的无监督几何表征融入扩散策略学习，实现几何感知决策。大量实验表明，Tele-Catch在动态抓取任务中显著提升了精度与鲁棒性，并在不同构型的灵巧手及未见物体类别上展现出一致的性能增益。