AnySkin: Plug-and-play Skin Sensing for Robotic Touch
This work addresses the problem of underutilized tactile sensing in robotics by providing a more accessible and generalizable sensor, though it appears incremental as it builds on prior designs like ReSkin.
The paper tackles the challenges of versatility, replaceability, and data reusability in tactile sensing for robotics by introducing AnySkin, a plug-and-play magnetic tactile sensor that simplifies integration and enables zero-shot generalization of learned manipulation policies to new instances, achieving competitive performance compared to existing solutions like DIGIT and ReSkin.
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/