Beyond Digital Intelligence: Physical AI and the Evolution of Embodied Machine Learning
(Room 303B)
05 Nov 25
12:15 PM
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12:40 PM
Tracks:
Connectivity and IoT - Protocols and Standards 2
Physical AI represents a paradigm shift from traditional digital intelligence toward embodied systems that perceive, reason, and act within physical environments through integrated sensorimotor capabilities. Through analysis of implementations across autonomous robotics, smart manufacturing, healthcare assistance, and environmental monitoring, this paper demonstrates that physical AI requires specialized architectures prioritizing sensorimotor integration, real-time environmental adaptation, and safety-critical decision-making over computational throughput. The research identifies three fundamental components: multimodal sensor fusion networks providing comprehensive environmental understanding, embodied learning algorithms that adapt through physical interaction, and robust actuator control systems enabling precise manipulation. Our findings reveal that physical AI systems exhibit emergent intelligence through continuous environmental interaction, developing capabilities that cannot be achieved through simulation or digital-only training. This research contributes to understanding how AI's future lies in creating intelligent systems that seamlessly bridge digital computation and physical reality, enabling machines to operate as autonomous agents in complex, dynamic environments.