从世界行动模型到具身大脑：开放世界物理智能路线图
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
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中文总结 AI 辅助
研究针对通用人工智能中物理世界推理行动的进展零散问题，提出以具身大脑为中心的物理智能协同进化路线图，利用世界行动模型等，通过物理框架、共享契约和闭环训练等构建模块化智能栈，推动物理智能发展。
中文摘要 AI 辅助
通用人工智能最终需要能在物理世界中推理和行动的智能体。行动模型、视觉语言行动策略和世界模型推动了这一目标，世界行动模型尤其有前景。但进展仍零散，存在模型角色与表示、目标与标准化、系统组成三方面耦合差距。基于此分析，提出以具身大脑为中心的物理智能协同进化路线图，它整合多模态上下文等，WAMs提供预测功能原型，物理框架通过工具等实现模型输出，共享契约对齐异构模型等，闭环训练将交互转化为经验，定义模块化物理智能栈。
英文摘要
Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have advanced this goal, while World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences. However, progress remains fragmented: models use incompatible action spaces and prediction targets, datasets and tasks follow different conventions, and runtime systems expose limited interfaces for reuse and evaluation. We review the evolution toward WAMs and organize these limitations into three coupled gaps: model roles and representations, objectives and standardization, and system composition. Building on this analysis, we propose a co-evolution roadmap for physical intelligence centered on the \emph{embodied brain}, a long-term model target for integrating multimodal context, comparing candidate interventions, and issuing state-transition or capability requests rather than direct actuator commands. WAMs provide promising prototypes for its predictive functions, while a physical harness grounds model outputs through tools, controllers, verification, and trace logging. Shared contracts align heterogeneous models, data, tasks, and embodiments, and closed-loop post-training converts verified interaction into reusable experience. Together, these components define a modular physical-intelligence stack for adaptive and self-improving embodied agents.
发表机构
- IEEE
机构由 AI 辅助整理，请以论文原文为准。
