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A Study on Recent Research Trends and Future Directions in Physical AI

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2025, 11(4), pp.107~116
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : July 18, 2025
  • Accepted : August 15, 2025
  • Published : August 31, 2025

KIM JONGHOON 1 EUIJIK KIM 2 Dong-Wan Kim 3

1동아대학교
2한림대학교
3동아대학교 전자공학과

Accredited

ABSTRACT

Physical AI refers to technologies enabling mechanical systems—such as robots and drones—to learn their operational dynamics and embed intelligence. This field has attracted significant attention in autonomous robotic systems engineering, where machines learn and execute complex tasks in real-world environments. In this paper, we compare QT-Opt, Dreamer, Gato, and RoboCat across algorithm, data requirements, computational complexity, Sim-to-Real, and limitations. We also review domain randomization and world-model–based self-supervised learning (SGF), and a surgical-robot case. Furthermore, we assess V-JEPA 2 and NVIDIA Cosmos in terms of data scale, use cases, and constraints. Finally, we discuss future development strategies based on the limitations identified in recent studies.

Citation status

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