Physical AI: Evolution, Progress, Challenges, and Prospects

Recent advancements in deep learning, high-fidelity simulation, and robotic hardware have propelled significant progress in Physical AI. This field marks a revolutionary step in the evolution of AI by com-bining the precision of physical laws with the adaptability of machine learning. In this paper, we review the development of Physical AI and its taxonomy by examining the relevant literature, categorizing it into three sub-domains: Physical-Informed AI, Generative Physical AI, and Embodied AI. These sub-domains primarily tackle scientific and engineering challenges, create physics-plausible scenarios, and enable robots or autonomous vehicles to interact with the physical world. This approach also ad-dresses the questions of how to perceive, generate, and interact with the physical world by integrating physics with AI algorithms. Additionally, we discuss related benchmarks and datasets. Finally, we out-line the current challenges and propose potential opportunities for future research.