  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.
