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Generative AI meets classical control: Hybrid few-shot PI tuning for autonomous robots

artificial-intelligence
autonomous-systems
data-mining-and-machine-learning
optimization-theory-and-computation
robotics

Abstract

Foundation models have remarkably few-shot learning and data-generation capabilities. We harness these to adaptively tune AGV PID controllers with minimal real-world data. Our few-shot transfer learning strategy tackles the tedious trial-and-error retuning required for new conditions. We train an ensemble regression model on initial AGV data, then use a pre-trained foundation model to generate synthetic control samples from a few new trials, augmenting the dataset. Fine-tuning the ensemble on this combined real and synthetic data enables rapid convergence to effective PI gains for changing scenarios while ensuring precise, stable navigation. Real-world AGV tests confirm robust tracking under varying speeds and cut manual retuning. This hybrid of generative AI and classical control is novel: unlike methods requiring extensive data or manual tweaks, ours uses AI-synthesized data for adaptive performance with minimal trials. Our results outline a new paradigm where foundation models and transfer learning boost robot adaptability and deployment efficiency.
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