Traditional control personalization requires users to understand optimization parameters and provide repetitive numerical feedback, creating significant barriers for non-expert users. To deal with this issue, we propose ChatMPC, a model predictive control framework that enables users to personalize control systems and adapt to environmental changes through natural language interaction. The framework operates in two modes: personalization, where users iteratively adjust control behavior to their preferences, and co-development, where users provide real-time environmental information that complements sensor data. We establish convergence guarantees under different user behavior models, demonstrating exponential convergence for consistent feedback and finite-time convergence with logarithmic interaction complexity for tolerance-based users. We validate ChatMPC through experiments in robot navigation with personalized obstacle avoidance and semi-autonomous driving with conversational obstacle reporting. Both experiments achieve real-time performance and demonstrate effective adaptation to user preferences and environmental changes.
@article{arxiv.2508.16913,
title = {Chat-Driven Reconfiguration of Model Predictive Control},
author = {Yuya Miyaoka and Masaki Inoue and Jos'e M Maestre},
journal= {arXiv preprint arXiv:2508.16913},
year = {2025}
}