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.

The reskin library can be found on GitHub at this link. The trained model from the paper can be downloaded here.

Materials used in the Fabrication video:

• Smooth-On Dragon Skin 10 NV
• Magnequench MQP-15-7-20065(~80 mesh) Bonded Neo Powder
• Thin plastic film (link is representative)

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

This template was originally made by Phillip Isola and Richard Zhang for a colorful ECCV project; the code can be found here.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.