融合软体与视觉触觉模拟，实现高精度机械手触感仿真
TacEx: GelSight Tactile Simulation in Isaac Sim -- Combining Soft-Body and Visuotactile Simulators
- 将GIPC软体接触模拟与Taxim/FOTS视觉触觉模型集成到Isaac Sim
- 支持物体推、举、平衡等任务的稳定触觉反馈训练
- 适合需要真实触感交互的机器人强化学习研究者
在仿真中训练机器人策略日益流行，但针对接触密集型操作任务的精确、可靠且易用的触觉模拟器仍属空白。为此，我们开发了TacEx——一个模块化触觉模拟框架。将先进的软体接触模拟器GIPC，以及基于视觉的触觉模拟器Taxim和FOTS，集成至Isaac Sim中，以实现对视觉触觉传感器GelSight Mini的稳健且逼真的模拟。我们构建了多个Isaac Lab强化学习环境，包括物体推移、抓举和杆子平衡任务。验证表明，仿真稳定，高维观测（如凝胶形变和GelSight相机的RGB图像）可用于训练。代码、视频及补充结果将在线发布于https://sites.google.com/view/tacex。
原文摘要 · Abstract (English)
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.
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