From Understanding the World to Intervening in It: A Unified Multi-Scale Framework for Embodied Cognition

2025-03-02

AI Summary

This work addresses the fragmentation of perception, memory, and decision-making in embodied cognition. We propose AUKAI, a multi-scale embodied cognitive framework that establishes a closed-loop integration of perception–memory–decision processes, incorporating cross-scale error feedback to jointly realize world modeling, state prediction, and intervention utility evaluation. To our knowledge, this is the first embodied intelligence architecture to rigorously unify convergence theory, optimal control, and Bayesian inference. Furthermore, we design a neuro-symbolic hybrid structure that enhances both interpretability and robustness without compromising performance. The framework is empirically validated on robot navigation and obstacle avoidance tasks, supporting deployment in both simulation and real-world environments. Theoretical analysis proves the system’s convergence, Lyapunov stability, and near-optimality under bounded uncertainty.

Technology Category

• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Develops a unified framework for embodied cognition integrating perception, memory, decision-making.
• Proposes AUKAI for predicting state transitions and evaluating intervention utility.
• Demonstrates AUKAI's application in robotic navigation and obstacle avoidance.

Innovation

• Adaptive Unified Knowledge-Action Intelligence (AUKAI)
• Multi-scale error feedback integration
• Hybrid neural-symbolic implementation