We present a novel approach for photorealistic robot simulation that integrates 3D Gaussian Splatting as a drop-in renderer within vectorized physics simulators such as IsaacGym. This enables unprecedented speed -- exceeding 100,000 steps per second on consumer GPUs -- while maintaining high visual fidelity, which we showcase across diverse tasks. We additionally demonstrate its applicability in a sim-to-real robotics setting. Beyond depth-based sensing, our results highlight how rich visual semantics improve navigation and decision-making, such as avoiding undesirable regions. We further showcase the ease of incorporating thousands of environments from iPhone scans, large-scale scene datasets (e.g., GrandTour, ARKit), and outputs from generative video models like Veo, enabling rapid creation of realistic training worlds. This work bridges high-throughput simulation and high-fidelity perception, advancing scalable and generalizable robot learning. All code and data will be open-sourced for the community to build upon. Videos, code, and data available at https://escontrela.me/gauss_gym/.
@article{arxiv.2510.15352,
title = {GaussGym: An open-source real-to-sim framework for learning locomotion from pixels},
author = {Alejandro Escontrela and Justin Kerr and Arthur Allshire and Jonas Frey and Rocky Duan and Carmelo Sferrazza and Pieter Abbeel},
journal= {arXiv preprint arXiv:2510.15352},
year = {2025}
}