ReSkin: versatile, replaceable, lasting tactile skins
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:4WM2JGGMrecord.jsonopen to challenge →
read the original 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.
This paper has not been read by Pith yet.
Forward citations
Cited by 4 Pith papers
-
Tactile Genesis: Exploring Tactile Sensors at Scale for Learning Dexterous Tasks
Tactile Genesis provides a scalable multi-type tactile simulator and ablation results showing whole-hand coverage with per-taxel force/torque sensing outperforms fingertip-only or other modalities across three dextero...
-
Dense Force Estimation with an Event-based Optical Tactile Sensor
Introduces a framework for dense 3D force field estimation from event-based optical tactile sensors by combining event marker tracking, a CNN for normal displacements, and inverse FEM.
-
TactX: Learning Shared Tactile Representations Across Diverse Sensors
TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and...
-
Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation
A visuo-tactile policy learning method that exploits tactile motion correlation for contact state distinction and Mixture-of-Transformers for cross-modal fusion.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.