Tele-Catch: Adaptive Teleoperation for Dexterous Dynamic 3D Object Catching

TL;DR

Tele-Catch introduces a shared autonomy framework combining human teleoperation with autonomous diffusion policies for dynamic 3D object catching, improving robustness and accuracy.

Contribution

The paper presents DAIM for adaptive control fusion and DP-U3R for geometry-aware policy learning, advancing dynamic object catching capabilities.

Findings

Significant accuracy improvements in dynamic catching tasks.

Robustness across different robotic hands and unseen objects.

Effective fusion of human input with autonomous policies.

Abstract

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.