物理 AI 模拟现状综述：仿真引擎与三计算机范式
原标题：The State of Simulation for Physical AI: An Overview
AI 摘要
Hugging Face 博客发布了一篇关于物理 AI 模拟现状的综述，指出数据稀缺是物理 AI 发展的主要挑战，而仿真通过 GPU 并行可低成本生成大量训练数据。文章介绍了训练、仿真和机载三计算机范式，并概述了 MuJoCo、MuJoCo Warp、NVIDIA Isaac Sim 和 Isaac Lab 等主流仿真引擎的特点与适用场景。该综述旨在帮助开发者根据需求选择合适的仿真工具，推动机器人学习与策略训练的发展。
正文节选
Figure 1: Humanoid robot locomotion simulation. The robot's pose is represented by tracked body keypoints (green markers), while successive robot instances illustrate its movement through time. Directional arrows indicate commanded motion, demonstrating the use of a physics-based simulation environment for training and evaluating robot locomotion and control policies. The primary challenge in building physical AI systems is data availability. Large language models (LLMs) and vision-language mode