From Understanding the World to Intervening in It: A Unified Multi-Scale Framework for Embodied Cognition
This work addresses the challenge of enabling AI systems to understand and act in the world, which is incremental as it builds on existing theories and methods in embodied cognition.
The paper tackles the problem of embodied cognition by proposing AUKAI, a unified framework that integrates perception, memory, and decision-making for predicting state transitions and evaluating interventions, with theoretical analysis and a hybrid implementation for robotic navigation and obstacle avoidance.
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.