NVIDIA Isaac Sim用GPU加速实现大规模高保真机器人仿真，提升数据生成效率。
NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics
- 基于GPU并行计算构建高并发仿真系统，支持大规模训练。
- 可生成高质量合成数据，缓解真实数据稀缺问题。
- 适合研究者用于机器人学习与仿真驱动的实验设计。
仿真已成为机器人研究的核心基础设施。与以往模拟器不同，NVIDIA Isaac Sim 利用 GPU 加速实现了大规模并行训练和高保真物理建模。其合成数据生成流水线缓解了高质量训练数据短缺问题，支持数据驱动的机器人学习和大规模仿真中心实验。然而，现有综述常将其视为众多模拟器之一，缺乏对其架构特性、使用模式及局限性的系统分析。本文从系统与应用角度综述 Isaac Sim，阐述其架构，并与主流模拟器进行对比。我们分析了五个主要领域中的代表性研究，总结常见使用模式，尤其在数据生成与高保真仿真方面。同时，提出关键未来方向与挑战，包括物理开放世界学习、仿真中心训练及实际可用性限制。
原文摘要 · Abstract (English)
Simulation has become a core infrastructure for robotics research. Unlike previous simulators, NVIDIA Isaac Sim leverages GPU acceleration to enable large-scale parallel training and physics-accurate modeling. Its synthetic data generation pipeline alleviates the scarcity of high-quality training data, supporting data-driven robot learning and large-scale simulation-centric experimentation. However, existing surveys often treat it as one simulator among many, without a systematic analysis of its architectural characteristics, usage patterns, and limitations. This survey reviews Isaac Sim from system and application perspectives, outlining its architecture and comparing it with widely used simulators. We analyze representative studies across five major domains and summarize common usage patterns, particularly in data generation and high-fidelity simulation. We also outline key future directions and challenges, including physics open-world learning, simulation-centric training and practical usability constraints.
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