Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917369570394112 |
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| author | Jia, Feiyu Niu, Xiaojie Yang, Sizhe Ben, Qingwei Huang, Tao zhao, Feng Wang, Jingbo Pang, Jiangmiao |
| author_facet | Jia, Feiyu Niu, Xiaojie Yang, Sizhe Ben, Qingwei Huang, Tao zhao, Feng Wang, Jingbo Pang, Jiangmiao |
| contents | Teleoperation is a key approach for collecting high-quality, physically consistent demonstrations for robotic manipulation. However, teleoperation for dexterous manipulation remains constrained by: (i) inaccurate hand-robot motion mapping, which limits teleoperated dexterity, and (ii) limited tactile feedback that forces vision-dominated interaction and hinders perception of contact geometry and force variation. To address these challenges, we present TAG, a low-cost glove system that integrates precise hand motion capture with high-resolution tactile feedback, enabling effective tactile-in-the-loop dexterous teleoperation. For motion capture, TAG employs a non-contact magnetic sensing design that provides drift-free, electromagnetically robust 21-DoF joint tracking with joint angle estimation errors below 1 degree. Meanwhile, to restore tactile sensation, TAG equips each finger with a 32-actuator tactile array within a compact 2 cm^2 module, allowing operators to directly feel physical interactions at the robot end-effector through spatial activation patterns. Through real-world teleoperation experiments and user studies, we show that TAG enables reliable real-time perception of contact geometry and dynamic force, improves success rates in contact-rich teleoperation tasks, and increases the reliability of demonstration data collection for learning-based manipulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28542 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation Jia, Feiyu Niu, Xiaojie Yang, Sizhe Ben, Qingwei Huang, Tao zhao, Feng Wang, Jingbo Pang, Jiangmiao Robotics Teleoperation is a key approach for collecting high-quality, physically consistent demonstrations for robotic manipulation. However, teleoperation for dexterous manipulation remains constrained by: (i) inaccurate hand-robot motion mapping, which limits teleoperated dexterity, and (ii) limited tactile feedback that forces vision-dominated interaction and hinders perception of contact geometry and force variation. To address these challenges, we present TAG, a low-cost glove system that integrates precise hand motion capture with high-resolution tactile feedback, enabling effective tactile-in-the-loop dexterous teleoperation. For motion capture, TAG employs a non-contact magnetic sensing design that provides drift-free, electromagnetically robust 21-DoF joint tracking with joint angle estimation errors below 1 degree. Meanwhile, to restore tactile sensation, TAG equips each finger with a 32-actuator tactile array within a compact 2 cm^2 module, allowing operators to directly feel physical interactions at the robot end-effector through spatial activation patterns. Through real-world teleoperation experiments and user studies, we show that TAG enables reliable real-time perception of contact geometry and dynamic force, improves success rates in contact-rich teleoperation tasks, and increases the reliability of demonstration data collection for learning-based manipulation. |
| title | Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.28542 |