发表机构
Tsinghua University; Stanford University; University of Cambridge（清华大学; 斯坦福大学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SoTa低成本电容式触觉皮肤，实现人机全手覆盖与共享布局，支持协同训练，在接触密集操作中显著提升成功率。
AI 中文摘要
越来越多的研究表明，触觉感知为机器人策略提供了接触信息，这些信息在灵巧操作中补充了视觉信息。然而，视觉-触觉机器人数据仍然稀缺：灵巧演示需要远程操作机器人，这限制了数据集规模。人类演示的收集成本要低得多，并且为扩展此类数据提供了途径，但前提是人类和机器人的手部都携带具有相应信号的触觉传感器。这要求传感器能够适应不同的手部几何形状，覆盖整个手部，并在不同实体之间共享通用布局。我们提出了SoTa，一种低成本电容式触觉皮肤，可在人类和机器人上提供全手覆盖，同时在对应的手指和手掌区域保持202个触觉单元的共享布局。我们的多层设计采用织物电极，能够以每张皮肤低于10美元的材料成本在内部制造具有可定制几何形状的薄而柔软的皮肤。该传感器在10,000次加载-卸载循环后保留了其初始响应范围的97%以上，并且其轨迹在1,280次紧握折叠循环中保持连续性。共享的触觉单元布局支持使用通用触觉编码器进行人机协同训练，无需学习跨传感器映射。在三个接触密集的操作任务中，触觉观测相比仅视觉策略提高了分布内成功率。在固定的机器人演示预算下，添加人类演示使八种评估条件下的平均成功率从22.8%提高到45.9%，提高了一倍以上，并在所有五种分布外条件下提高了成功率。我们计划开源制造和操作这些皮肤所需的资源。
英文摘要
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.
CommentsThe first three authors contributed equally. Project website: https://sota-skin.github.io
