Embodied AI

Embodied AI emphasizes physical grounding - AI systems that learn and develop intelligence through bodily interaction with environments. The robot's sensors (cameras, force sensors, proprioception) provide rich multimodal input about physical interactions. Actions have real consequences, creating feedback loops that shape learning. Embodied AI agents build internal world models by manipulating objects and observing results. Sensorimotor learning connects perception and action - learning that pulling opens doors, that smooth objects are slippery, that soft objects deform. This physical grounding enables intuitive physics understanding that purely virtual AI lacks. The body becomes integral to cognition, not just a vessel.

Embodied AI enables humanoid robots to learn object affordances through interaction - understanding that handles enable grasping, buttons invite pressing. Navigation systems build spatial understanding by physically moving through spaces. Manipulation skills develop through tactile exploration and feedback. Social robots learn appropriate physical interactions (handshakes, hugs) through embodied practice. Tool use emerges from physical experimentation. Embodied language grounding connects words to physical experiences - learning "heavy" by lifting objects, "red" by seeing colors.