Physical AI

A text model can generate a sentence without moving matter. A robot action changes contact forces, object position, battery state and risk around people. Physical AI therefore combines model output with geometry, dynamics, timing and control. The same command can produce different results when friction, lighting, payload or object shape changes.

Embodied AI studies intelligence expressed through a body that senses and acts. The body determines reachable space, camera viewpoint, joint limits, gripper geometry and available force. A policy trained on one robot may not transfer directly to another because the observation and action spaces differ.

A VLA model receives visual observations and language, then predicts actions or action tokens. The model may select end-effector motion, joint targets or higher-level skills. Conventional controllers still execute and stabilize the movement, and safety software may reject commands that exceed limits.

Robot foundation models are trained across many tasks, scenes or robot datasets so they can be adapted instead of trained from zero for every behavior. Their useful scope must be measured by the robots, tasks and environments represented in the data. Broad pretraining does not guarantee reliable transfer to a new embodiment or contact-rich task.

A world model represents possible future states after an action. In robotics, prediction can support planning, synthetic data and evaluation of candidate actions. Prediction quality alone is insufficient because the robot must convert a chosen future into motor commands and verify the result through new observations.

Cameras, depth sensors, LiDAR, force sensors, tactile arrays, encoders and inertial sensors provide partial measurements. Perception software estimates objects, geometry, motion, contact and robot pose. Errors propagate into planning, so uncertainty and sensor failure must be handled explicitly.

Recognizing a cup is different from lifting it without slipping, crushing it or colliding with another object. Manipulation requires grasp geometry, force control, compliance, trajectory planning and feedback after contact. Deformable objects and clutter create additional uncertainty.

Human demonstrations can be recorded through teleoperation, motion capture, kinesthetic teaching or video. A useful dataset aligns observations, actions, timing and task labels. Demonstrations often contain operator habits and coverage gaps, so data quality and task diversity matter as much as quantity.

Teleoperation lets people demonstrate a task and intervene when autonomy fails. Logged camera, joint, action and force data can support imitation learning. Latency, operator skill and interface design influence the recorded behavior, and personal or workplace data requires consent and privacy controls.

Simulation can generate many scenes, object positions and failure cases while protecting physical equipment. It supports reinforcement learning, regression testing and synthetic sensor data. The reality gap remains because contacts, materials, lighting, actuator response and wear are approximated.

Synthetic images, trajectories and scenes can fill rare conditions or reduce collection cost. Teams still need physical tests to detect modeling errors and distribution shifts. Synthetic data should be documented so benchmark results do not imply that the same conditions were measured on hardware.

In closed-loop control, the robot acts, receives new sensor data and updates the command. Higher inference frequency can reduce the time between observation and correction, but total system response also depends on camera rate, preprocessing, communication, controller timing and actuator dynamics.

A multi-step task may fail even when each individual skill works most of the time. Object displacement, grip error, scene changes and uncertain state accumulate across steps. Long-horizon evaluation should report full-task success, recovery behavior and where human intervention occurred.

Deployment requires monitoring, updates, permissions, network security, maintenance, charging and procedures for blocked or damaged robots. A model benchmark does not measure these operational requirements. The complete system must be tested in the environment where it will work.

Models can fail on unfamiliar objects, ambiguous instructions and sensor conditions outside the training distribution. Hardware can overheat, lose calibration or reach force and speed limits. Responsible reporting separates model capability from the physical platform and states whether evidence comes from simulation or real robots.