Artificial intelligence in robotics has seen immense progress, yet integrating high-level reasoning, robust interpretation of sensory data, and long-range planning remains a grand challenge. We introduce the Reasoning, Interpreting, and Planning Adoption Model (REPAM), a novel AI-driven robotics framework that unifies chain-of-thought reasoning, multimodal interpretation, and MuZero-style planning in a modular architecture. REPAM leverages large pre-trained models for interpretable decision chains, domain randomization for sim-to-real robustness, and a learned world model for strategic planning, addressing critical gaps in adaptability and generalization. This paper delineates REPAM’s design and theoretical underpinnings, situates it within the landscape of classical control, learned policies, and foundation models, and evaluates its alignment with real-world requirements. We discuss how REPAM can enable robots to tackle complex tasks with human-like reasoning and foresight while adhering to safety and compliance (e.g., GDPR, HIPAA) through transparent operations. By synthesizing case studies and historical milestones, we demonstrate REPAM’s potential to advance autonomous systems toward greater generality, interpretability, and trustworthiness in high-stakes environments.