用手套+力矩反馈实现精准灵巧操作，提升抓取成功率与效率
Glovity: Learning Dexterous Contact-Rich Manipulation via Spatial Wrench Feedback Teleoperation System
- 通过触觉手套与空间力矩反馈装置，实现实时力感知
- 书本翻页任务成功率从48%提升至78%，耗时减少25%
- 适合需要精细力控的机器人操作研究者使用
我们提出Glovity，一种低成本可穿戴遥操作系统，融合空间力矩（力-扭矩）反馈装置与配备指尖霍尔传感器校准的触觉手套，实现高保真灵巧操作反馈。该系统通过直观的力矩与触觉反馈解决高接触任务中的挑战，并通过精确重定向克服身体映射差异。用户实验表明：力矩反馈使书本翻页任务成功率从48%提升至78%，完成时间减少25%；指尖校准显著优于商用手套在薄物体抓取中的表现。此外，将力信号融入模仿学习（通过DP-R3M），在新型高接触场景中取得高成功率，如自适应页面翻转和力感知交接。所有软硬件设计将开源。项目官网：https://glovity.github.io/
原文摘要 · Abstract (English)
We present Glovity, a novel, low-cost wearable teleoperation system that integrates a spatial wrench (force-torque) feedback device with a haptic glove featuring fingertip Hall sensor calibration, enabling feedback-rich dexterous manipulation. Glovity addresses key challenges in contact-rich tasks by providing intuitive wrench and tactile feedback, while overcoming embodiment gaps through precise retargeting. User studies demonstrate significant improvements: wrench feedback boosts success rates in book-flipping tasks from 48% to 78% and reduces completion time by 25%, while fingertip calibration enhances thin-object grasping success significantly compared to commercial glove. Furthermore, incorporating wrench signals into imitation learning (via DP-R3M) achieves high success rate in novel contact-rich scenarios, such as adaptive page flipping and force-aware handovers. All hardware designs, software will be open-sourced. Project website: https://glovity.github.io/
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