Physical Artificial Intelligence for Powering the Next Revolution in Robotics

Abstract

Abstract Physical artificial intelligence (AI) is driving the next revolution in robotics by grounding perception, action, and cognition within a robot’s physical structure. Unlike traditional systems that rely on disembodied reasoning and preprogrammed control, physical AI leverages sensorimotor coupling to enable real-time adaptation, experiential learning, and generalized task performance. Advances in machine learning, high-fidelity simulations, and multimodal sensing have accelerated progress toward real-world deployment. This position article articulates a unifying perspective on physical AI, outlining its conceptual evolution, defining system-level principles, and analyzing key functional subsystems, such as situational awareness, mapping, planning, control, and human–robot interaction. It provides a domain-wise readiness assessment across manufacturing, healthcare, logistics, agriculture, service robotics, and space exploration, highlighting opportunities and limitations. Finally, it identifies critical challenges—real-time performance, cybersecurity, benchmarking, safety, interpretability, and energy efficiency—and proposes codesign principles and evaluation frameworks to guide future research. By synthesizing these elements, the article positions physical AI as a foundational paradigm for trustworthy, adaptive, and mission-ready robotic systems, offering readers a roadmap for research priorities, cross-domain insights, and practical implications that will shape the next era of robotics.