Physical AI enables robots and autonomous systems to perceive, reason, and act in the physical world. Unlike traditional AI models that operate primarily on text, images, or digital environments, Physical AI combines perception, decision-making, and actions through robotics, embodied AI, and VLA models.

Physical AI systems rely on multimodal datasets, including visual data, motion capture data, teleoperation recordings, trajectory data, sensor fusion data, human demonstrations, and environmental interactions. These datasets help robots learn perception, manipulation, navigation, and task execution.

Motion capture (MOCAP) data captures human movements and interactions that can be used for robot learning, imitation learning, locomotion, and manipulation training. Common methods include IMU-based motion capture, markerless tracking, and IR marker systems.

3D LiDAR annotation involves labeling objects and environments within point cloud data to help robots and autonomous systems understand spatial relationships. It is widely used for robot perception, autonomous navigation, obstacle detection, and sensor fusion applications.

Teleoperation and human demonstrations provide robots with examples of task execution, object manipulation, and decision-making behaviors. These datasets help train Physical AI models to perform complex actions more efficiently and improve task success rates.