Core Insights - The article discusses the integration of world models into embodied intelligent systems, emphasizing the shift from reactive to predictive capabilities in these systems [1][3][8]. Summary by Sections Introduction to World Models - Embodied intelligent systems traditionally relied on a reactive loop of "perception-action" and lacked predictive capabilities. The introduction of world models allows these systems to "imagine" future scenarios [1][3]. Research Overview - A comprehensive survey from a research team including institutions like Tsinghua University and Harbin Institute of Technology categorizes existing research into three paradigms based on architectural integration [3][5]. Paradigm Classification - The relationship between world models (WM) and policy models (PM) is described as a "coupling strength spectrum," ranging from weak to strong dependencies [11]. - Three categories are identified: Modular, Sequential, and Unified architectures, each with distinct characteristics regarding gradient flow and information dependency [12]. Modular Architecture - In this architecture, WM and PM are independent, with no gradient flow between them. WM acts as a simulator, predicting future states based on current observations and candidate actions [16]. Sequential Architecture - This architecture involves two stages where WM predicts future states, and PM executes actions based on those predictions. It simplifies complex tasks into goal generation and goal-conditioned execution [17][18]. Unified Architecture - The unified architecture integrates WM and PM into a single end-to-end network, allowing for simultaneous training and optimization. This structure enables the system to predict future states and generate actions without explicitly separating simulation and decision-making [19][21]. Future Directions - The article outlines potential research directions, including the selection of representation spaces for world models, the generation of structured intentions, and the need for unified world-policy model paradigms to enhance decision-making efficiency [22][24].

Core Insights - The article discusses the integration of world models into embodied intelligent systems, emphasizing the shift from reactive loops to predictive capabilities [2][10] - It highlights the importance of world models in enhancing sample efficiency, long-term reasoning, safety, and proactive planning in embodied agents [11][12] Summary by Sections Introduction to World Models - Embodied intelligent systems traditionally relied on a "perception-action" loop, lacking the ability to predict future states [2] - The introduction of world models allows agents to "imagine" future scenarios, enhancing their operational capabilities [10] Research Overview - A comprehensive survey from a research team involving multiple universities presents a framework for integrating world models into embodied systems [5][7] - The paper categorizes existing research into three paradigms based on architectural integration [5][14] Paradigm Classification - The relationship between world models (WM) and policy models (PM) is described as a "coupling strength spectrum," ranging from weak to strong dependencies [15] - Three categories are identified: Modular, Sequential, and Unified architectures, each with distinct characteristics [15][16] Modular Architecture - In this architecture, WM and PM operate as independent modules with weak coupling, focusing on causal relationships between actions and states [20] - The world model acts as an internal simulator, allowing agents to predict outcomes based on potential actions [20] Sequential Architecture - This architecture involves a two-stage process where WM predicts future states, and PM executes actions based on those predictions [21] - The world model generates a valuable goal, simplifying complex long-term tasks into manageable sub-problems [22][23] Unified Architecture - The unified architecture integrates WM and PM into a single end-to-end network, allowing for joint training and optimization [24][25] - This configuration enables the agent to anticipate future states and produce appropriate actions without explicitly separating simulation and decision-making [25] Future Directions - The article outlines potential research directions, including the representation space of world models, structured intent generation, and the balance between interpretability and optimality [27][28][29] - It emphasizes the need for effective alignment mechanisms to ensure performance while exploring unified world-policy model paradigms [29]