让机械手精准接住飞动物体，靠人机协同智能控制。
Tele-Catch: Adaptive Teleoperation for Dexterous Dynamic 3D Object Catching
- 用动态感知机制融合手套信号与扩散模型，实时调节抓取动作。
- 在多种手型和新物体上准确率显著提升，抗干扰能力更强。
- 适合需要灵巧操作的机器人应用，如工业分拣、服务机器人。
遥操作是将人类灵巧性转移给机器人的重要范式，但以往研究多聚焦于初始静止物体的抓取或操作。对于运动中物体的捕捉任务，纯遥操作常因时序、姿态和力控误差而失败，亟需结合人类输入与自主策略的共享自治。为此，我们提出 Tele-Catch 框架，用于灵巧手在动态物体捕捉中的遥操作。核心设计 DAIM 是一种动态感知自适应融合机制，将基于手套的遥操作信号融入扩散策略去噪过程，根据物体状态自适应调节控制。为提升策略鲁棒性，引入 DP-U3R，将点云观测的无监督几何表征整合进扩散策略学习，实现几何感知决策。大量实验表明，Tele-Catch 显著提升了动态捕捉任务的准确性与鲁棒性，并在不同灵巧手形态及未见过的物体类别上均表现稳定提升。
原文摘要 · Abstract (English)
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.
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