Paper ID: 2305.12605

Beyond Flat GelSight Sensors: Simulation of Optical Tactile Sensors of Complex Morphologies for Sim2Real Learning

Daniel Fernandes Gomes, Paolo Paoletti, Shan Luo

Recently, several morphologies, each with its advantages, have been proposed for the \textit{GelSight} high-resolution tactile sensors. However, existing simulation methods are limited to flat-surface sensors, which prevents their usage with the newer sensors of non-flat morphologies in Sim2Real experiments. In this paper, we extend a previously proposed GelSight simulation method developed for flat-surface sensors and propose a novel method for curved sensors. In particular, we address the simulation of light rays travelling through a curved tactile membrane in the form of geodesic paths. The method is validated by simulating the finger-shaped GelTip sensor and comparing the generated synthetic tactile images against the corresponding real images. Our extensive experiments show that combining the illumination generated from the geodesic paths, with a background image from the real sensor, produces the best results when compared to the lighting generated by direct linear paths in the same conditions. As the method is parameterised by the sensor mesh, it can be applied in principle to simulate a tactile sensor of any morphology. The proposed method not only unlocks simulating existing optical tactile sensors of complex morphologies but also enables experimenting with sensors of novel morphologies, before the fabrication of the real sensor. Project website: https://danfergo.github.io/geltip-sim

Submitted: May 21, 2023