Physical Artificial Intelligence for Powering the Next Revolution in Robotics
Atul Thakur, Krishnanand N. Kaipa, Ashis G. Banerjee, David J. Cappelleri, Venkat Krovi, Satyandra K. Gupta
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.