What Is Physical AI?

Physical AI combines AI models, real-world data, simulation, and accelerated computing to help autonomous machines interpret their surroundings and take action.

Unlike AI systems that primarily work with digital information, physical AI must account for spatial relationships, changing conditions, and the rules that govern the physical world. It can use multimodal inputs, including images, video, language, speech, and sensor data, to inform an autonomous machine’s decisions and actions.

Developers can use open models, datasets, and software to inspect, test, customize, and post-train systems for their domain. They use physically based simulation and synthetic data to train and validate models across diverse, rare, and safety-critical scenarios before deploying them in robots, vehicles, and industrial environments.

The three-computer framework describes three connected stages: training AI models, simulating and validating autonomous systems, and running trained models on real-world machines. Together, these stages help teams develop physical AI for robotaxis and autonomous vehicles, robotics, and industrial AI.

Train and Fine-Tune Models

Training systems process large real-world and synthetic datasets to develop and post-train models for perception, reasoning, planning, and action. Open models can give developers greater ability to inspect, test, customize, and govern models for their specific environments and tasks.

Simulate and Validate Systems

Simulation systems create physically based virtual environments where teams can train, test, and evaluate physical AI before deployment. Because diverse real-world data can be slow, costly, or unsafe to collect, world models and simulation can help create varied scenarios and synthetic data. For robotaxis and autonomous vehicles, teams can evaluate rare and safety-critical road situations.

Digital twins represent real machines and spaces, helping teams test how robots, sensors, and AI agents interact across a facility, including fleet movements, layouts, and workflows. Simulation can also support reinforcement learning and imitation learning for robots. Synthetic data, evaluation results, and operational data can inform further training and validation.

Run AI on Autonomous Machines

Runtime systems enable robots, vehicles, and industrial machines to process sensor data and make decisions in real time. They connect perception, reasoning, planning, and action so machines can operate in dynamic physical environments.

Safety spans all three stages, with validation, monitoring, and evidence integrated throughout development and deployment.

Physical AI applications span robotics, autonomous vehicles and robotaxis, and industrial AI and manufacturing.

Physical AI elevates robots from rigid automation to autonomy that can generalize to new environments and tasks.

• Autonomous mobile robots in warehouses can navigate complex environments and avoid obstacles and workers, using direct feedback from onboard sensors.
• Manipulators, or robot arms, can adjust their grasping strength and position based on the pose of objects on a conveyor belt.
• Surgical robots can learn intricate tasks such as threading needles and performing stitches.
• Humanoid robots need both gross and fine motor skills, plus the ability to move, perceive, understand, reason, and interact with the physical world.

Physical AI helps robotaxis and other autonomous vehicles process sensor data to perceive and understand changing road conditions. Reasoning vision-language-action models can connect perception, planning, and action to support decisions in complex driving situations. Training and validating AVs in physically based simulation helps teams evaluate rare and safety-critical scenarios that are difficult or dangerous to reproduce on public roads.

Physical AI helps improve the safety and efficiency of factories and warehouses, where people, vehicles, and robots share dynamic environments. Fixed cameras and computer vision can support operational awareness and route planning by tracking multiple entities and activities. Video analytics AI agents can help detect anomalies and provide real-time alerts. These insights can inform industrial facility digital twins, helping teams simulate workflows, evaluate layouts, and optimize routes before implementing changes in the physical facility.