构建具身AI的内心世界：全面综述
Modeling the Mental World for Embodied AI: A Comprehensive Review
AI总结：
本文综述了具身AI中内心世界模型的研究，构建了理论框架，定义了关键组件，并分析了两种核心理论思维推理范式，以促进人机协作发展。
摘要：
随着具身AI代理在虚拟角色、可穿戴设备和机器人系统中的应用不断深入，其核心研究挑战已逐渐从物理环境交互转向对社会互动的准确理解。传统的物理世界模型（PWM）专注于可量化物理属性，如空间和运动，无法满足社会智能建模的需求。相比之下，内心世界模型（MWM）作为对人类内部心理状态的结构化表示，已成为具身代理实现自然人机协作和动态社会适应的关键认知基础。然而，当前MWM研究面临显著瓶颈：例如概念框架碎片化，MWM与PWM之间的边界模糊，不同理论思维（ToM）推理范式的技术路径和适用场景的推理机制不连贯，以及评估与实践之间的脱节。为解决这些问题，本文系统综合了超过100项权威研究，提供了一个全面的MWM研究综述。其核心贡献有三个方面：首先，它首次构建了完整的MWM理论框架，具体区分了MWM与PWM的本质差异。其次，它通过两种心理元素表示范式系统定义了MWM的关键组成部分。第三，它全面分析了两种核心ToM推理范式，包含19种ToM方法。最后，它还明确了神经符号混合架构的整合趋势，并综合了26个ToM评估基准。这项工作旨在促进具身代理融入人类社会，并推动人机协作交互的深入发展。
英文摘要：
As the application of Embodied AI Agents in avatars, wearable devices, and robotic systems continues to deepen, their core research challenges have gradually shifted from physical environment interaction to the accurate understanding of social interactions. Traditional physical world models (PWM) focus on quantifiable physical attributes such as space and motion, failing to meet the needs of social intelligence modeling. In contrast, the Mental World Model (MWM), as a structured representation of humans' internal mental states, has become the critical cognitive foundation for embodied agents to achieve natural human-machine collaboration and dynamic social adaptation. However, current MWM research faces significant bottlenecks: such as fragmented conceptual framework with vague boundaries between MWM and PWM, disjointed reasoning mechanisms for the technical pathways and applicable scenarios of different Theory of Mind (ToM) reasoning paradigms, and detachment between evaluation and practice. To address these issues, this review systematically synthesizes over 100 authoritative studies to provide a comprehensive overview of MWM research for embodied AI. Its core contributions are threefold: First, it constructs a complete theoretical framework for MWM for the first time. Specifically, it distinguishes the essential differences between MWM and PWMs. Second, it systematically defines the key components of MWM through two paradigms for mental element representation. Third, it comprehensively analyzes two core ToM reasoning paradigms with 19 ToM methods. Finally, it also clarifies the integration trend of neuro-symbolic hybrid architectures, and synthesizes 26 ToM evaluation benchmarks. This work aims to promote the integration of embodied agents into human society and advance the in-depth development of human-machine collaborative interaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
