  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.
