Continuity and Drift in AI Robotics: Foundations for Reliable and Adaptive Systems

This paper introduces continuity-aware AI as a foundational principle for robotic systems, addressing the limitations of scale-only approaches in modern autonomous agents. It formalizes identity horizons, decision lineage, and drift metrics as operational tools for maintaining temporal coherence, intent alignment, and authority in AI-driven robotics. Through illustrative examples and hypothetical scenarios, the work demonstrates how embedding continuity transforms reactive, brittle robots into self-monitoring, context-aware, and predictable agents, capable of: Early detection and correction of behavioral drift Safe adaptation across new tasks, environments, and human collaborators Multi-agent coordination and scalable deployment Auditability and alignment with ethical and regulatory standards The framework bridges practical robotics, machine learning, and governance, offering a pathway toward resilient, accountable, and enduring autonomous systems. The paper highlights future research directions, including multi-sensory integration, continuity-driven meta-cognition, and cross-domain knowledge transfer.