ReSkin: versatile, replaceable, lasting tactile skins
This work addresses the need for durable and scalable tactile sensors in robotics, offering a practical solution for long-term use, though it is incremental in improving existing soft sensor technology.
The paper tackled the problem of soft tactile sensors deteriorating quickly and having variable responses, which limits their longevity and replaceability, by introducing ReSkin, a low-cost, replaceable tactile sensor that uses magnetic sensing and machine learning to achieve robust performance across fabrication and time variations.
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.