外观
论文arXiv 2610.2117113 天前
Physical AI：模型、学习、系统和开放挑战综述
原文链接：Physical AI: A Survey of Models, Learning, Systems, and Open Challenges（arXiv 2610.21171 · 发布日期：2026-10-15）
Physical AI: A Survey of Models, Learning, Systems, and Open Challenges
该综述面向Physical AI可靠自主性，按物理交互闭环系统梳理模型、学习、系统、评估与安全。
核心要点
- Physical AI需在部分可观测、不确定动力学、具身约束和实时要求下与物理世界交互。
- 论文按物理交互闭环组织，综述任务表示与记忆、预测和动作生成模型等基础与进展。
- 还覆盖数据与学习、具身与控制、计算系统、评估和安全等系统组成。
- 从集成视角提出泛化、高效学习执行、状态跟踪、安全、恢复和持续适应等挑战。
英文摘要 (Abstract)
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.
作者：Bin Gao, Anuj Gautam, Aritra Bagchi, Yunkai Liang, Wentao Li, Ronghui Li, Zhi Zhou, Yancan Mao, Xiao Yan, Xin He
来源
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