High-fidelity simulation is essential for robotics research, enabling safe and efficient testing of perception, control, and navigation algorithms. However, achieving both photorealistic rendering and accurate physics modeling remains a challenge. This paper presents a novel simulation framework, the Unreal Robotics Lab (URL), that integrates the advanced rendering capabilities of the Unreal Engine with MuJoCo's high-precision physics simulation. Our approach enables realistic robotic perception while maintaining accurate physical interactions, facilitating benchmarking and dataset generation for vision-based robotics applications. The system supports complex environmental effects, such as smoke, fire, and water dynamics, which are critical to evaluating robotic performance under adverse conditions. We benchmark visual navigation and SLAM methods within our framework, demonstrating its utility for testing real-world robustness in controlled yet diverse scenarios. By bridging the gap between physics accuracy and photorealistic rendering, our framework provides a powerful tool for advancing robotics research and sim-to-real transfer. Our open-source framework is available at https://unrealroboticslab.github.io/.

翻译：高保真模拟对于机器人研究至关重要，能够安全高效地测试感知、控制和导航算法。然而，同时实现照片级逼真渲染与精确物理建模仍是一大挑战。本文提出了一种新型仿真框架——虚幻机器人实验室（URL），该框架将虚幻引擎的先进渲染能力与MuJoCo的高精度物理模拟相结合。我们的方法既能实现逼真的机器人感知，又能保持精确的物理交互，从而为基于视觉的机器人应用提供基准测试和数据集生成支持。该系统可模拟烟雾、火焰和水动力学等复杂环境效应，这对于评估机器人在恶劣条件下的性能至关重要。我们在框架内对视觉导航和SLAM方法进行了基准测试，证明了其在受控但多样化的场景中测试真实世界鲁棒性的实用性。通过弥合物理精确性与照片级逼真渲染之间的鸿沟，我们的框架为推进机器人研究和仿真到现实的迁移提供了强大工具。我们的开源框架可通过https://unrealroboticslab.github.io/获取。