AnySkin让机器人触觉传感器像手机壳一样即插即用，还能跨设备通用。
AnySkin: Plug-and-play Skin Sensing for Robotic Touch
- 采用磁性吸附设计，无需胶水，可快速更换传感器
- 首次实现无需校准的跨实例触觉策略泛化，性能优于DIGIT和ReSkin
- 适合需要快速部署触觉系统的机器人研发人员
尽管触觉感知被广泛认为是重要且有用的传感模态，但其应用远不及视觉和本体感知。AnySkin解决了阻碍触觉感知推广的关键问题：多功能性、可替换性和数据复用性。基于ReSkin的简约设计，将传感电子与感应界面分离，使集成如同贴手机壳、插充电器般简单。此外，AnySkin是首个报告学习到的操作策略具备跨实例泛化能力的非校准触觉传感器。本工作有三项核心贡献：第一，提出一种简化制造流程与设计工具，实现无胶粘、耐用且易更换的磁性触觉传感器；第二，表征了滑动检测与策略学习效果；第三，演示了在单一实例上训练的模型可零样本泛化至新实例，并与主流触觉方案DIGIT和ReSkin进行对比。实验视频、制造细节与设计文件详见https://any-skin.github.io/
原文摘要 · Abstract (English)
While tactile sensing is widely accepted as an important and useful sensing modality, its use pales in comparison to other sensory modalities like vision and proprioception. AnySkin addresses the critical challenges that impede the use of tactile sensing -- versatility, replaceability, and data reusability. Building on the simplistic design of ReSkin, and decoupling the sensing electronics from the sensing interface, AnySkin simplifies integration making it as straightforward as putting on a phone case and connecting a charger. Furthermore, AnySkin is the first uncalibrated tactile-sensor with cross-instance generalizability of learned manipulation policies. To summarize, this work makes three key contributions: first, we introduce a streamlined fabrication process and a design tool for creating an adhesive-free, durable and easily replaceable magnetic tactile sensor; second, we characterize slip detection and policy learning with the AnySkin sensor; and third, we demonstrate zero-shot generalization of models trained on one instance of AnySkin to new instances, and compare it with popular existing tactile solutions like DIGIT and ReSkin. Videos of experiments, fabrication details and design files can be found on https://any-skin.github.io/
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