Physical artificial intelligence

The term became increasingly prominent during the AI boom in the 2020s as AI development expanded from primarily digital applications toward humanoid robots, self-driving vehicles, smart factories and other autonomous machines. Its boundaries are not standardized, and it is often treated as a continuation of earlier research in robotics and embodied intelligence rather than an entirely separate field.

Operation

Physical AI systems commonly operate through a continuous cycle of perception, planning and action. Cameras, lidar, radar, microphones and tactile or motion sensors collect information about the environment. Computer vision, machine vision, sensor fusion and simultaneous localization and mapping may be used to identify objects, estimate their positions and construct a representation of the surrounding world.

Open-source libraries such as OpenCV and Dlib provide computer-vision, image-processing and machine learning components that can be incorporated into perceptual systems. These libraries provide individual software components rather than complete autonomous systems.

After interpreting its surroundings, a system may use task planning, motion planning or learned policies to select an action. Control software converts the plan into commands for robot locomotion for motors, robotic joints or other actuators. New sensor information allows the system to evaluate the result and modify its plan as environmental conditions change.