フィジカルAI：モデル・学習・システム・未解決課題のサーベイ
Physical AI: A Survey of Models, Learning, Systems, and Open Challenges
物理世界で知覚・推論・行動・学習を行うフィジカルAIについて、モデルや学習手法、実装システム、評価、安全性までを閉ループの物理相互作用の観点から横断的に整理したサーベイ。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Bin Gao, Anuj Gautam, Aritra Bagchi, Yunkai Liang, Wentao Li, Ronghui Li, Zhi Zhou, Yancan Mao, Xiao Yan, Xin He, Qingyun Zou, Wenshuo Yue, Dan Chen, Zhanglu Yan, Fei Xu, Huize Li, Weijie Wang, Tao Ouyang, Tulika Mitra
分類: eess.SY
原文アブストラクト
Physical AI aims to enable intelligent systems to perceive, reason, act, and learn through interaction with the physical world. Unlike purely digital intelligence, physical agents must operate under partial observability, uncertain dynamics, embodiment constraints, and real-time requirements, while their actions continuously reshape the environment and subsequent observations. Recent advances in foundation models and robot learning have expanded the capabilities of physical agents, yet achieving reliable autonomy requires more than improvements in individual models. This survey presents a comprehensive, system-level review of Physical AI, organized around the closed-loop process of physical interaction. We examine the foundations and recent developments in task representations and memory, predictive and action-generating models, data and learning methods, embodiment and control, computing systems, evaluation, and safety. Beyond reviewing individual advances, we investigate how these components interact and collectively shape the capabilities and limitations of Physical AI systems. Building on this integrated perspective, we identify fundamental challenges in generalization, resource-efficient learning and execution, reliable state tracking, safety, recovery, and continual adaptation. We argue that advancing Physical AI requires moving beyond isolated model performance toward integrated systems capable of robust, efficient, safe, and autonomous physical interaction under changing real-world conditions.
関連論文
- 継続観測と適時介入による能動的フィジカルAIフィジカルAI
- フィジカルAIの能力形成における7つの源泉フィジカルAI