AnySkin:用于机器人触摸的即插即用皮肤传感技术

Raunaq Bhirangi, Venkatesh Pattabiraman, Enes Erciyes, Yifeng Cao, Tess Hellebrekers, Lerrel Pinto
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引用次数: 0

摘要

虽然触觉传感作为一种重要而有用的传感方式已被广泛接受,但与视觉和本体感觉等其他传感方式相比,其应用却显得微不足道。AnySkin解决了阻碍触觉传感应用的关键难题--多功能性、可替换性和数据可重用性。AnySkin以ReSkin的简洁设计为基础,将传感电子元件与传感接口分离,简化了集成过程,使其就像装上手机壳和连接充电器一样简单。总之,这项工作有三个主要贡献:首先,我们介绍了一种简化的制造工艺和设计工具,用于制造一种无胶、耐用且易于更换的磁性触觉传感器;其次,我们描述了使用AnySkin传感器进行滑动检测和策略学习的特点;第三,我们演示了在AnySkin的一个实例上训练的模型对新实例的零次泛化,并将其与DIGIT和ReSkin等现有流行触觉解决方案进行了比较。https://any-skin.github.io/。
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AnySkin: Plug-and-play Skin Sensing for Robotic Touch
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.https://any-skin.github.io/
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