Physical AI lets autonomous systems like cameras, robots, and self-driving cars perceive, understand, reason, and perform or orchestrate complex actions in the physical world.
Previously, autonomous machines were unable to perceive and sense the world around them. But with physical AI, robots can be built and trained to seamlessly interact with and adapt to their surroundings in the real world.
To build physical AI, teams need powerful, physics-based simulations that provide a safe, controlled environment for training autonomous machines. This not only enhances the efficiency and accuracy of robots in performing complex tasks but also facilitates more natural interactions between humans and machines, improving accessibility and functionality in real-world applications.
Physical AI is unlocking new capabilities that will transform every industry. For example:
Robots: Physical AI elevates robots from rigid automation to true autonomy. Enabling them to sense, reason, and act in real time helps them perform with greater safety, precision, and adaptability in any environment.
Autonomous Vehicles (AVs): Physical AI lets AVs process sensor data in real-time to perceive and understand their surroundings. Reasoning vision-language-action (VLA) models use this data to make informed decisions in various environments, from open freeways to urban cityscapes. Training AVs in scalable, physically accurate simulation environments helps them more accurately detect pedestrians, respond to traffic or weather conditions, and autonomously navigate lane changes, effectively adapting to a wide range of unexpected scenarios.
Smart Spaces: Physical AI is enhancing the functionality and safety of large indoor and outdoor spaces like factories and warehouses, where daily activities involve steady traffic of people, vehicles, and robots. Using fixed cameras and advanced computer vision models, teams can enhance dynamic route planning and optimize operational efficiency by tracking multiple entities and activities within these spaces. Video analytics AI agents further improve safety and operational efficiency by automatically detecting anomalies and providing real-time alerts.
Generative AI models such as GPT and Llama are trained on enormous amounts of text and image data, largely gathered from the internet. These AI models have astonishing capabilities in producing human language, visuals, and abstract concepts, but they're limited in their grasp of the physical world and its rules.
Physical AI extends current generative AI with an understanding of spatial relationships and the physical behavior of the 3D world we all live in. It takes multimodal inputs such as images, videos, text, speech, or real-world sensor data and converts them into insights or actions that an autonomous machine can execute.
Training physical AI models requires large, diverse, and physically accurate data about the spatial relationships and physical rules of the real world. Collecting this data in real-world settings can be tedious, error-prone, dangerous, and expensive. The combined use of simulation and world foundation models (WFMs) can supercharge the creation of synthetic data for training physical AI models.
Data generation starts with the creation of a digital twin of a space, such as a factory. Real-world sensor data can also be brought directly into interactive simulations using 3D Gaussian-based reconstruction. In this virtual space, sensors and autonomous machines like robots are added. Simulations that mimic real-world scenarios are performed, and the sensors capture various interactions like rigid body dynamics—such as movement and collisions—or how light interacts in an environment. The generated data can then be augmented, curated, and annotated with WFMs.
Reinforcement learning teaches autonomous machines skills in a simulated environment to perform in the real world. It allows autonomous machines to learn safely and quickly through thousands, or even millions, of trial-and-error tasks.
This learning technique rewards a physical AI model for successfully completing desired actions in the simulation, so the model continuously adapts and improves. With repeated reinforcement learning, autonomous machines eventually adapt to new situations and unforeseen challenges, preparing them to operate in actual field conditions. Over time, an autonomous machine can develop the sophisticated fine motor skills needed for real-world applications, such as packing boxes, building vehicles, and navigating environments unassisted.
Universal Scene Description (OpenUSD) plays a central role in physical AI by providing a universal data standard across multiple industries. This enables interoperability, real-time collaboration, seamless integration, and efficient management of complex 3D environments.
Additionally, SimReady assets in OpenUSD embed both physical and semantic properties, making assets immediately simulation-ready for realistic AI interaction and high-fidelity training.
Building the next generation of autonomous systems using physical AI involves a coordinated process across multiple, specialized computers.
1. Training Computer: NVIDIA DGX
NVIDIA DGX™ is a fully integrated hardware and software AI platform that provides the massive computational power required to train physical AI foundation models. Developers can use real or synthetically generated data to train or post-train foundation models with frameworks such as TensorFlow, PyTorch, Cosmos Curator, or NVIDIA TAO, along with pretrained computer vision models available on NVIDIA NGC. DGX systems enable robots to understand natural language, recognize objects, and plan complex movements simultaneously through intensive model training.
2. Simulation and Synthetic Data Generation Computer: NVIDIA Omniverse With Cosmos on NVIDIA RTX PRO Servers
Construct Virtual 3D Environments
A high-fidelity, physically based virtual environment is needed to represent the real environment and generate the synthetic data necessary for training physical AI. NVIDIA Omniverse™ is a platform of APIs, SDKs, and services that lets developers integrate OpenUSD and NVIDIA RTX™ rendering technologies into existing software tools and simulation workflows to build these 3D environments. To create these digital twins, real-world sensor data can be brought directly into simulation using NVIDIA Omniverse NuRec neural reconstruction libraries. NuRec enables developers to reconstruct scenes, render interactive simulations, and use generative AI to enhance reconstruction quality, bridging the gap between the real world and simulation.
Generate Synthetic Data
In addition to accurately reflecting the physics and behaviors of the real world, simulated environments for physical AI must also match the diversity of everyday interactions and scenarios. Use Omniverse Replicator for environment and object domain randomization. Render the randomized scenes as images or videos, then use NVIDIA Cosmos™ models to augment, curate, and annotate the generated data to scale a single scenario into hundreds.
Training and Validating Robot Policies in Simulation
Simulation provides a way to train robots various skills like manipulating objects or traversing a space. These skills can be refined in NVIDIA Isaac Lab, a modular robot learning framework through reinforcement or imitation learning.
Once trained, the model and its software stack can be validated in simulation using reference open-source robotics simulation frameworks like NVIDIA Isaac Sim™ or the open-source CARLA AV simulator. A large fleet of robots can be simulated and tested using "Mega," an NVIDIA Omniverse Blueprint.
3. Runtime Computer: NVIDIA Jetson Thor
Finally, the optimized stack and policy model can be deployed on NVIDIA Jetson™ or NVIDIA DRIVE AGX™ to run embedded in the autonomous robot, vehicle, or smart space. Jetson Thor's compact design provides the computational power needed to process sensor data, reason, plan and execute actions within milliseconds for real-time autonomous robot operation. Build video analytics AI agents with Metropolis AI Blueprint for video search and summarization to provide oversight on factory performance and safety.