Physical AI connects perception and understanding to movement, interaction and learning.
The physical autonomy cycle is easier to understand as four connected modes: understand the world, move through it, interact with it and improve from experience.
Robots perceive equipment, structures and physical conditions without relying on fixed inspection paths.
Build persistent spatial representations of environments that continue to change over time.
Understand assets, environments and operational changes through repeated physical observation.
Identify unexpected physical states using multimodal sensing and contextual reasoning.
Move through complex environments while continuously updating routes, constraints and risk.
Move assets across operational environments with context-aware planning and control.
Perceive, plan, grasp and interact with physical objects that are not perfectly repeatable.
Improve models and behavior from real-world experience, evaluation and deployment feedback.
