物理AI仿真的现状：概述

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 models (VLMs) can be trained on internet-scale datasets, but robotics and physical AI systems do not have the same advantage. To train a physical AI system, a robot must learn the consequences of interacting with the physical world. For example, it needs to understand what happens when a cup slips, a cable bends, or a gripper contacts an object at the wrong angle. Collecting this kind of data in the real world is slow, expensive, risky, and sometimes impractical due to the destructive nature of the tasks. Simulation provides a bridge by enabling developers to generate large amounts of photorealistic, physically grounded data. By teleoperating robots in simulation and scaling data collection through GPU parallelism, developers can generate thousands of hours of robot experience at a fraction of the cost of real-world collection. Earlier robotics simulators were often used primarily to debug geometry, test controllers, or visualize robot motion. Today, simulation has become part of the model development loop. Teams use it to generate perception datasets, train reinforcement learning policies, collect demonstrations, augment real-world data, benchmark models, and test policies against rare or adversarial scenarios. This shift is why industrial research labs and academic groups are increasingly contributing to, or developing, simulation engines that can meet these new requirements. These requirements can be understood through a three-computer paradigm: Training computer: A large GPU cluster used to process data and train foundation AI models. Simulation computer: A GPU workstation or cluster using GPU-accelerated physics and RTX rendering to generate robot experience, sensor data, and simulated interactions. On-robot computer: An edge device, such as a NVIDIA Jetson AGX Thor-class system , that runs the trained policy or model during deployment. Figure 2 : A physical system (Earth and robot) continuously exchanges data with its virtual representation (digital model), enabling monitoring, analysis, prediction, and control through a bidirectional feedback loop. Each computer plays a different role depending on the task’s latency, throughput, accuracy, and deployment requirements. Many simulation engines are available today, each targeting different use cases such as humanoid robots, aerial vehicles, ground robots, autonomous vehicles, quadrupeds, and dexterous manipulators. These engines also differ in their support for reinforcement learning, batched simulation, contact-rich physics, photorealistic rendering, and sensor simulation. Refer to Figure 2 for a categorization of simulation engines by robot domain and acceleration profile. Developers often struggle with which simulation engine to use for the use case they are tackling. Oftentimes, this falls on a few fundamental questions, such as