Authors: Raunaq Bhirangi, Venkatesh Pattabiraman, Enes Erciyes, Yifeng Cao, Tess Hellebrekers, Lerrel Pinto
Abstract: 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/
Source: http://arxiv.org/abs/2409.08276v1
