可穿戴机器人系统实现力觉-视觉-触觉协同学习，提升操作鲁棒性。
AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
- 设计可穿戴设备AetheRock，集成触觉、力觉与视觉传感器
- 提出ForceVT框架，使触觉学习不依赖高精度传感器
- 实验证明系统在传感器不一致下仍具高效性，适合实际应用
接触丰富的操作任务中，力觉与触觉感知至关重要。然而，手持或可穿戴设备中触觉与力觉传感器的不兼容限制了力感知机器人的学习效果。为此，我们提出AetheRock——一种佩戴于手臂的机器人教学系统，包含指尖处的模块化可视触觉传感器GelSlim-MiniFab、人手指接触区域的电阻式压力传感器、定制PCB模块及舒适耐用的穿戴套件，用于采集夹持力、视觉与触觉数据。基于此，我们进一步提出ForceVT表示学习框架，利用力觉与视觉信息引导无保真度要求的触觉学习，实现任意触觉场景下的稳健推理。真实世界实验表明，AetheRock具备良好的数据效率，且ForceVT有效缓解了因触觉传感器制造与使用不一致导致的学习低效问题。整体上，本工作通过创新硬件设计与算法，缓解了夹持力-视觉-触觉机器人学习的局限性。
原文摘要 · Abstract (English)
Force and tactile sensing are indispensable in contact-rich manipulation. However, force-aware robot learning faces critical challenges due to the incompatible assembly of tactile and force sensors in handheld or wearable devices. To address these limitations, we first introduce AetheRock for gripper-force, vision, and tactile data collection, which is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip, a resistive pressure sensor at the human finger contact region, a customized PCB module, and a wearable kit for comfortable and robust collection. Building on this, we propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning, enabling robust inference in any tactile situation. Real-world experiments show that AetheRock achieves qualified data efficiency and that ForceVT effectively alleviates inefficiencies when visuo-tactile sensors exhibit manufacturing and utilization inconsistencies. Overall, our work mitigates the limitations of gripper-force vision-tactile robot learning through innovative hardware design and algorithms.
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