Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Maijunxian Wang

TL;DR

The work proposes AUKAI, an Adaptive Unified Knowledge-Action Intelligence framework for embodied cognition that fuses perception, memory, and decision-making via multi-scale error feedback. It treats the agent as an embedded world model that jointly predicts state transitions and evaluates intervention utility, with a formal objective that minimizes prediction error while maximizing utility, under a contraction-map–based convergence and Lyapunov-stability analysis. AUKAI integrates neural and symbolic components to enable explicit causal reasoning and uncertainty handling, and provides a detailed multi-scale robotic navigation example to illustrate practical deployment. The paper contributes a rigorous theoretical foundation, architecture design guidance, and a comprehensive experimental plan to validate convergence, robustness, and interpretability in both simulations and real-world tasks, aiming to advance robust, adaptable embodied cognition for AGI.

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.

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

TL;DR

The work proposes AUKAI, an Adaptive Unified Knowledge-Action Intelligence framework for embodied cognition that fuses perception, memory, and decision-making via multi-scale error feedback. It treats the agent as an embedded world model that jointly predicts state transitions and evaluates intervention utility, with a formal objective that minimizes prediction error while maximizing utility, under a contraction-map–based convergence and Lyapunov-stability analysis. AUKAI integrates neural and symbolic components to enable explicit causal reasoning and uncertainty handling, and provides a detailed multi-scale robotic navigation example to illustrate practical deployment. The paper contributes a rigorous theoretical foundation, architecture design guidance, and a comprehensive experimental plan to validate convergence, robustness, and interpretability in both simulations and real-world tasks, aiming to advance robust, adaptable embodied cognition for AGI.

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.

Paper Structure

This paper contains 45 sections, 17 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Related Work
  3. Perception and Representation Learning
  4. Memory and Temporal Dynamics
  5. Reinforcement Learning and Decision-Making
  6. Multi-Scale Modeling and Error Feedback
  7. Neural-Symbolic Integration
  8. Theoretical Foundations: Convergence, Stability, and Optimal Control
  9. Summary and Gap Analysis
  10. Framework Architecture and Methodology
  11. Parameter Spaces and Relationships
  12. Unified Model and Overall Objective
  13. Symbol, Parameter, and Loss Definitions
  14. Memory
  15. World Model and Intervention Utility
  16. ...and 30 more sections

Figures (1)

  • Figure 1: AUKAI Framework: Forward flow (solid) and key error feedback (dashed).