DenseTact 2.0: Optical Tactile Sensor for Shape and Force Reconstruction

TL;DR

DenseTact 2.0 is an optical tactile sensor that visualizes soft fingertip deformation to accurately reconstruct shapes and estimate forces, enhancing dexterous robotic manipulation.

Contribution

The paper introduces DenseTact 2.0, a novel optical tactile sensor that combines visual deformation analysis with neural networks for high-precision shape and force sensing.

Findings

Shape reconstruction accuracy of 0.3633mm per pixel

Force estimation accuracy of 0.410N

Transfer learning reduces data requirements by 88%

Abstract

Collaborative robots stand to have an immense impact on both human welfare in domestic service applications and industrial superiority in advanced manufacturing with dexterous assembly. The outstanding challenge is providing robotic fingertips with a physical design that makes them adept at performing dexterous tasks that require high-resolution, calibrated shape reconstruction and force sensing. In this work, we present DenseTact 2.0, an optical-tactile sensor capable of visualizing the deformed surface of a soft fingertip and using that image in a neural network to perform both calibrated shape reconstruction and 6-axis wrench estimation. We demonstrate the sensor accuracy of 0.3633mm per pixel for shape reconstruction, 0.410N for forces, 0.387Nmm for torques, and the ability to calibrate new fingers through transfer learning, which achieves comparable performance with only 12% of the non-transfer learning dataset size.