Embodied Artificial Intelligence for Long-Horizon Planning and Adaptive Control in Complex Environments
Keywords:
Embodied AI, Long-Horizon Planning, Adaptive Control, Hierarchical Reinforcement LearningAbstract
Embodied Artificial Intelligence represents a transformative paradigm in modern computer science, shifting the focus from passive computational models to active agents capable of perceiving, reasoning, and acting within complex physical or simulated environments. A central challenge in this domain is the integration of long-horizon planning with adaptive control, as agents must consistently execute extended sequences of actions while simultaneously reacting to dynamic, non-stationary environmental perturbations. This paper presents a comprehensive theoretical and empirical investigation into a novel hierarchical framework designed to bridge the gap between high-level semantic reasoning and low-level motor execution. By decoupling the temporal abstraction of tasks, our architecture allows a global planning module to generate subgoals over extended time horizons, while a localized adaptive controller utilizes continuous sensory feedback to execute these subgoals robustly. We extensively analyze the underlying mathematical formulations, state-space representations, and algorithmic implementations necessary to achieve this synthesis. Furthermore, we conduct rigorous experimental evaluations within high-fidelity simulated physics environments to benchmark our proposed system against existing state-of-the-art methodologies. The results demonstrate significant improvements in task success rates, path efficiency, and robustness to variations in environmental dynamics. This research provides a foundational step toward the deployment of highly autonomous embodied agents in real-world scenarios, offering critical insights into the scalable design of resilient control systems.
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