用虚幻引擎与物理引擎融合，打造高保真机器人仿真环境。
Unreal Robotics Lab: A High-Fidelity Robotics Simulator with Advanced Physics and Rendering
- 结合虚幻引擎渲染与MuJoCo物理模拟，实现视觉与物理双重真实
- 支持烟雾、火焰、水流等复杂环境，测试机器人在恶劣条件下的表现
- 适合做视觉导航、SLAM算法测试，助力仿真到现实的迁移
高保真仿真对机器人研究至关重要，可安全高效地测试感知、控制和导航算法。然而，实现逼真的视觉渲染与精确的物理建模仍具挑战。本文提出全新仿真框架Unreal Robotics Lab（URL），将虚幻引擎的先进渲染能力与MuJoCo的高精度物理仿真相结合。该方法在保持物理交互准确性的同时，实现逼真机器人感知效果，适用于基于视觉的机器人应用的基准测试与数据集生成。系统支持烟雾、火焰、水体动力学等复杂环境效应，有助于评估机器人在极端条件下的性能。我们在框架内对视觉导航与SLAM方法进行基准测试，验证其在可控但多样场景中评估实际鲁棒性的价值。通过弥合物理精度与视觉真实之间的差距，本框架为推动机器人研究与仿真到现实的迁移提供了强大工具。开源代码已发布于https://unrealroboticslab.github.io/。
原文摘要 · Abstract (English)
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/.
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