AI · Robotics
Physical AI: from simulation and GPUs to action in the real world
Physical AI links perception, reasoning and control, but deployment still depends on deterministic safety layers and operational data.
The stack
Cameras and sensors capture the scene; perception models identify objects and state; planners select actions; controllers translate them into motion. Vision-language-action models seek to generalize across instructions and scenes. Safety-rated control, limits and emergency functions remain separate from probabilistic AI behavior.
Training and simulation
Demonstration data, teleoperation, synthetic data and reinforcement learning can train policies. Simulation and digital twins create many scenarios without risking equipment, but reality gaps remain: friction, lighting, deformable objects and human behavior can differ from the model. Validation must return to physical operations.
Infrastructure
GPU clusters accelerate training and synthetic-data generation. Cloud supports data pipelines and fleet learning; edge computers provide low-latency inference near the robot. Connectivity moves telemetry and updates, but essential safe behavior cannot depend on an uninterrupted external network. Cybersecurity spans devices, models, updates and fleet management.