Understanding how artificial systems can develop spatial awareness and reasoning has long been a challenge in AI research. Traditional models often rely on passive observation, but embodied cognition theory suggests that deeper understanding emerges from active interaction with the environment. This study investigates whether neural networks can autonomously internalize spatial concepts through interaction, focusing on planar navigation tasks. Using Gated Recurrent Units (GRUs) combined with Meta-Reinforcement Learning (Meta-RL), we show that agents can learn to encode spatial properties like direction, distance, and obstacle avoidance. We introduce Hybrid Dynamical Systems (HDS) to model the agent-environment interaction as a closed dynamical system, revealing stable limit cycles that correspond to optimal navigation strategies. Ridge Representation allows us to map navigation paths into a fixed-dimensional behavioral space, enabling comparison with neural states. Canonical Correlation Analysis (CCA) confirms strong alignment between these representations, suggesting that the agent's neural states actively encode spatial knowledge. Intervention experiments further show that specific neural dimensions are causally linked to navigation performance. This work provides an approach to bridging the gap between action and perception in AI, offering new insights into building adaptive, interpretable models that can generalize across complex environments. The causal validation of neural representations also opens new avenues for understanding and controlling the internal mechanisms of AI systems, pushing the boundaries of how machines learn and reason in dynamic, real-world scenarios.

本研究解决了人工智能系统如何通过与环境的主动互动来发展空间意识的问题。采用结合了门控递归单元和元强化学习的新方法，我们展示了智能体能够自主内化空间概念，并且建立了混合动力系统模型，揭示了与最佳导航策略相对应的稳定极限环。这项工作为连接人工智能中的行动与感知提供了新视角，并推动了机器在动态复杂环境中的学习和推理能力的边界。

身体化世界模型在开放环境中的导航任务中形成