ReSkin: versatile, replaceable, lasting tactile skins
Raunaq Bhirangi*
CMU
Tess Hellebrekers*
FAIR
Carmel Majidi
CMU
Abhinav Gupta
CMU/FAIR
* equal contribution
Conference on Robot Learning (CoRL), 2021

Abstract

Soft sensors have continued growing interest in robotics, due to their ability to enable both passive conformal contact from the material properties and active contact data from the sensor properties. However, the same properties of conformal contact result in faster deterioration of soft sensors and larger variations in their response characteristics over time and across samples, inhibiting their ability to be long-lasting and replaceable. ReSkin is a tactile soft sensor that leverages machine learning and magnetic sensing to offer a low-cost, diverse and compact solution for long-term use. Magnetic sensing separates the electronic circuitry from the passive interface, making it easier to replace interfaces as they wear out while allowing for a wide variety of form factors. Machine learning allows us to learn sensor response models that are robust to variations across fabrication and time, and our self-supervised learning algorithm enables finer performance enhancement with small, inexpensive data collection procedures. We believe that ReSkin opens the door to more versatile, scalable and inexpensive tactile sensation modules than existing alternatives.


Talk



Source Code

The reskin library can be found on GitHub at this link.


Design and Fabrication



Paper and Bibtex

Citation
Raunaq Bhirangi, Tess Hellebrekers, Carmel Majidi and Abhinav Gupta. ReSkin: versatile, replaceable, lasting tactile skins. CoRL 2021.



Acknowledgements

This template was originally made by Phillip Isola and Richard Zhang for a colorful ECCV project; the code can be found here. We would also like to acknowledge Plan2Explore for their derived design which also helped develop this website.

Acknowledgements