外观
论文arXiv 2610.1255820 天前
PanTact：用可穿戴接触界面教授全身灵巧性
为将人形机器人操作扩展到手以外，PanTact从无机器人真人示教学习全身接触，并在Unitree G1上验证。
宇树 G1原文链接：PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface（arXiv 2610.12558 · 发布日期：2026-10-08）
PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface
为将人形机器人操作扩展到手以外，PanTact从无机器人真人示教学习全身接触，并在Unitree G1上验证。
核心要点
- 人形机器人有多自由度和接触面，但现有移动操作多限制在双手并避免身体接触。
- PanTact将接触作为跨形态操作信息，利用接触位置与时序描述完成任务所需交互。
- 系统结合可穿戴采集界面、视觉运动策略和接触感知全身控制，预测并实现全身接触目标。
- 在Unitree G1上无需额外身体接触传感器，展示支撑、推、扫和分担负载等全身灵巧性。
验证范围与局限：验证范围为Unitree G1人形机器人上的身体支撑、推动、清扫和负载分担等任务。
英文摘要 (Abstract)
Humanoid robots have many degrees of freedom and contact surfaces, yet most loco-manipulation research restricts manipulation to the hands and avoids other body contacts with the environment. To extend manipulation beyond the hands, we present PanTact, a system that learns humanoid whole-body dexterity, the capability to manipulate with any suitable body surface, directly from robot-free human demonstrations. Our key idea is to use contact as manipulation-centric information that transfers across embodiments. Contact locations and timing specify the interactions needed to accomplish a task, while the robot chooses how to coordinate its body to realize them within its own physical constraints. Specifically, PanTact captures, learns, and executes whole-body contact via: 1) a wearable, robot-free data collection interface that augments egocentric vision and bimanual UMI grippers with whole-body modular contact patches to record body contact locations and states; 2) a visuomotor policy that mitigates the egocentric observation gap and predicts whole-body contact goals; and 3) contact-aware whole-body control formulations that realize active contact goals using suitable robot surfaces. We deploy PanTact on a Unitree G1 humanoid without additional body contact sensors, demonstrating whole-body dexterity involving body support, pushing, sweeping, and load-sharing. Website: https://pantact.github.io
作者：Xiaomeng Xu, Zhang, Han, Zhian Ruan, Xiaofeng Guo, Jinkun Cao, Ruihan Yang, Carmelo Sferrazza, Shuran Song, Guanya Shi
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