Meta-Learning and Autonomous Cognitive AI Agents for Adaptive Manufacturing: Few-Shot Model Adaptation, Continual Learning, and Foundation Model-Driven Rapid Deployment

Abstract

Manufacturing environments are characterized by constant change — new product variants, shifted material suppliers, updated process parameters, evolving quality specifications — yet each change forces data-driven AI systems to undergo costly and time-consuming retraining cycles. The paradigm of meta-learning — learning to learn — offers a transformative solution: rather than training AI models from scratch on each new task, meta-learning enables models to adapt to novel manufacturing scenarios with minimal training data, by leveraging prior knowledge about the structure of learning problems across related tasks. Simultaneously, the emergence of foundation models — large-scale pre-trained AI models that can be adapted to diverse downstream tasks through fine-tuning or prompting — provides a new substrate for manufacturing AI, in which a single model pre-trained on broad industrial data can be rapidly specialized to specific production lines, products, or quality requirements. This review provides a comprehensive synthesis of meta-learning, few-shot adaptation, continual learning, and foundation model-driven AI for adaptive manufacturing, examining meta-learning algorithms and their manufacturing applications, few-shot adaptation for new product introduction and rapid deployment, continual learning for concept drift and production evolution, foundation model-driven manufacturing AI, autonomous cognitive AI agents for self-directed manufacturing intelligence, and the integration of meta-learning with the four preceding Yi Bao AI frameworks (RL-MPC, Adaptive Manipulation, Quality Intelligence Architecture, and Neuromorphic Industrial Intelligence Architecture). We further connect these advances to industrial optical sensing technologies — precision 3D surface metrology and four-dimensional thermal imaging — demonstrating how adaptive learning enables intelligent sensing systems to generalize across novel measurement tasks. A central contribution is the articulation of an integrated Adaptive Manufacturing Intelligence Architecture (AMIA) that unifies meta-learning adaptation, continual learning, foundation model fine-tuning, and autonomous cognitive agency for the next generation of self-improving manufacturing AI.

Keywords: Meta-Learning; Few-Shot Learning; Continual Learning; Foundation Models; Manufacturing AI; Adaptive AI; Rapid Deployment; Lifelong Learning; Model Adaptation;