In this paper, we propose AUKAI, an Adaptive Unified Knowledge-Action Intelligence for embodied cognition that seamlessly integrates perception, memory, and decision-making via multi-scale error feedback. Interpreting AUKAI as an embedded world model, our approach simultaneously predicts state transitions and evaluates intervention utility. The framework is underpinned by rigorous theoretical analysis drawn from convergence theory, optimal control, and Bayesian inference, which collectively establish conditions for convergence, stability, and near-optimal performance. Furthermore, we present a hybrid implementation that combines the strengths of neural networks with symbolic reasoning modules, thereby enhancing interpretability and robustness. Finally, we demonstrate the potential of AUKAI through a detailed application in robotic navigation and obstacle avoidance, and we outline comprehensive experimental plans to validate its effectiveness in both simulated and real-world environments.

翻译：本文提出AUKAI（自适应统一知识-行动智能体），一种通过多尺度误差反馈无缝整合感知、记忆与决策的具身认知框架。将AUKAI解释为嵌入式世界模型，我们的方法能同时预测状态转移并评估干预效用。该框架以收敛理论、最优控制与贝叶斯推理的严格理论分析为基础，共同确立了收敛性、稳定性及近似最优性能的条件。此外，我们提出一种融合神经网络与符号推理模块的混合实现方案，从而增强可解释性与鲁棒性。最后，通过机器人导航与避障的详细应用案例展示AUKAI的潜力，并规划了在仿真与真实环境中验证其有效性的完整实验方案。