提出可自适应整合感知与决策的机器人认知框架，实现对环境的预测与干预评估。
From Understanding the World to Intervening in It: A Unified Multi-Scale Framework for Embodied Cognition
- 通过多尺度误差反馈统一感知、记忆与决策流程。
- 理论证明框架具备收敛性、稳定性和近优性能。
- 融合神经网络与符号推理，提升可解释性与鲁棒性，适合机器人导航应用。
本文提出AUKAI——一种用于具身认知的自适应统一知识-行动智能框架，通过多尺度误差反馈无缝集成感知、记忆与决策。将AUKAI视为嵌入式世界模型，该方法同时预测状态转移并评估干预效用。框架基于收敛性理论、最优控制与贝叶斯推断的严格分析，确立了收敛、稳定及近优性能的条件。此外，我们提出一种混合实现方式，结合神经网络与符号推理模块，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展示其潜力，并规划在仿真与真实环境中全面验证其有效性。
原文摘要 · Abstract (English)
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.
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