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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.09229 |
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| _version_ | 1866915544465145856 |
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| author | Gao, Yuyang Ma, Haofei Zheng, Pai |
| author_facet | Gao, Yuyang Ma, Haofei Zheng, Pai |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09229 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Glovity: Learning Dexterous Contact-Rich Manipulation via Spatial Wrench Feedback Teleoperation System Gao, Yuyang Ma, Haofei Zheng, Pai Robotics 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/ |
| title | Glovity: Learning Dexterous Contact-Rich Manipulation via Spatial Wrench Feedback Teleoperation System |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.09229 |