DenseTact 2.0: Optical Tactile Sensor for Shape and Force Reconstruction

Won Kyung Do, Bianca Jurewicz, Monroe Kennedy

Collaborative robots stand to have an immense impact both on human welfare in domestic service applications and industrial superiority in advanced manufacturing requires 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, 0.387mmNm for torques, and the ability to calibrate new fingers through transfer learning, achieving comparable performance that trained more than four times faster with only 12% of the dataset size.

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