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/

翻译：尽管触觉传感被广泛认为是一种重要且实用的感知模态，但其使用频率仍远低于视觉和本体感觉等其他感知模态。AnySkin针对阻碍触觉传感应用的三个关键挑战——通用性、可替换性和数据可重用性——提出了解决方案。基于ReSkin的极简设计，并将传感电子元件与传感界面解耦，AnySkin简化了集成过程，使其变得如同安装手机保护壳并连接充电器一样简单直接。此外，AnySkin是首个无需校准且能够实现学习到的操作策略在跨实例间泛化的触觉传感器。总结而言，本工作做出了三项关键贡献：首先，我们引入了一种简化的制造流程和一个设计工具，用于创建无粘合剂、耐用且易于更换的磁性触觉传感器；其次，我们利用AnySkin传感器进行了滑移检测与策略学习的性能表征；最后，我们展示了在单个AnySkin实例上训练的模型能够零样本泛化到新实例，并将其与DIGIT、ReSkin等现有主流触觉解决方案进行了对比。实验视频、制造细节及设计文件可在 https://any-skin.github.io/ 获取。