TacEx: GelSight Tactile Simulation in Isaac Sim – Combining Soft-Body and Visuotactile Simulators

Duc Huy Nguyen, Guillaume Duret, Tim Schneider, Alap Kshirsagar, Boris Belousov, Jan Peters

Keywords: Tactile sensing, simulation, GelSight, Reinforcement Learning, Isaac Sim, Robotics

TL;DR: Extending Isaac Sim to include state of the art tactile simulators and GIPC soft body simulation for Reinforcement Learning.

Abstract: Training robot policies in simulation is becoming increasingly popular; nevertheless, a precise, reliable, and easy-to-use tactile simulator for contact-rich manipulation tasks is still missing. To close this gap, we develop TacEx – a modular tactile simulation framework. We embed a state-of-the-art soft-body simulator for contacts named GIPC and vision-based tactile simulators Taxim and FOTS into Isaac Sim to achieve robust and plausible simulation of the visuotactile sensor GelSight Mini. We implement several Isaac Lab environments for Reinforcement Learning (RL) leveraging our TacEx simulation, including object pushing, lifting, and pole balancing. We validate that the simulation is stable and that the high-dimensional observations, such as the gel deformation and the RGB images from the GelSight camera, can be used for training. The code, videos, and additional results will be released online https://sites.google.com/view/tacex.

Submission Number: 33