arXiv：cs.LG· Jingyun Yang, Baiyu Shi, Timothy Yu, Haitian Liu, Alberta Longhini, Weichen Wang, Rika Antonova, Zhenan Bao, Jeannette Bohg·· 3 小时前AI 评分56
SoTa：面向灵巧操作的软体触觉皮肤研究
SoTa: Soft Tactile Skins for Dexterous Manipulation
AI 导读
斯坦福团队在 arXiv 发布 SoTa，一种低成本电容式触觉皮肤，可在人手和机器人手上实现全手覆盖，并保持 202 个 taxel 的共享布局。材料成本每片不到 10 美元，经 10,000 次加载循环后仍保留超过 97% 的初始响应幅度。
正文
Abstract:A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation. However, visuo-tactile robot data remains scarce: dexterous demonstrations require teleoperating robots, which limits dataset scale. Human demonstrations are far cheaper to collect and offer a path to scale this data, but only if human and robot hands carry tactile sensors with corresponding signals. This requires sensors that conform to different hand geometries, cover the full hand, and share a common layout across embodiments. We present SoTa, a low-cost capacitive tactile skin that provides full-hand coverage on humans and robots while preserving a shared layout of 202 taxels across corresponding finger and palm regions. Our multilayer design with fabric electrodes enables in-house fabrication of thin, soft skins with customizable geometry for under $10 in materials per skin. The sensor retains over 97% of its initial response span after 10,000 loading-unloading cycles with traces retaining continuity through 1,280 tight-fist folding cycles. The shared taxel layout supports human-robot co-training with a common tactile encoder and no learned cross-sensor mapping. Across three contact-rich manipulation tasks, tactile observations improve in-distribution success over vision-only policies. With a fixed robot demonstration budget, adding human demonstrations more than doubles mean success across eight evaluation conditions, from 22.8% to 45.9%, improving success in all five out-of-distribution conditions. We plan to open-source the resources needed to fabricate and operate these skins.
| Comments: | The first three authors contributed equally. Project website: this https URL |
| Subjects: | Robotics (cs.RO); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02338 [cs.RO] |
| (or arXiv:2610.02338v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02338 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jingyun Yang [view email]
[v1]
Thu, 1 Oct 2026 18:08:59 UTC (9,101 KB)
来源：arXiv：cs.LG · arxiv.org