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.

翻译：在仿真环境中训练机器人策略正日益普及；然而，针对接触密集型操作任务，目前仍缺乏精确、可靠且易用的触觉仿真器。为填补这一空白，我们开发了TacEx——一个模块化的触觉仿真框架。我们将先进的软体接触仿真器GIPC以及基于视觉的触觉仿真器Taxim和FOTS嵌入Isaac Sim中，实现了对视觉触觉传感器GelSight Mini的鲁棒且逼真的仿真。我们基于TacEx仿真构建了多个用于强化学习（RL）的Isaac Lab环境，包括物体推挤、抓举和杆平衡任务。实验验证表明，该仿真系统运行稳定，且高维观测数据（如凝胶形变和GelSight相机采集的RGB图像）可用于策略训练。相关代码、视频及补充结果将通过https://sites.google.com/view/tacex在线发布。