High-speed off-road autonomous driving presents unique challenges due to complex, evolving terrain characteristics and the difficulty of accurately modeling terrain-vehicle interactions. While dynamics models used in model-based control can be learned from real-world data, they often struggle to generalize to unseen terrain, making real-time adaptation essential. We propose a novel framework that combines a Kalman filter-based online adaptation scheme with meta-learned parameters to address these challenges. Offline meta-learning optimizes the basis functions along which adaptation occurs, as well as the adaptation parameters, while online adaptation dynamically adjusts the onboard dynamics model in real time for model-based control. We validate our approach through extensive experiments, including real-world testing on a full-scale autonomous off-road vehicle, demonstrating that our method outperforms baseline approaches in prediction accuracy, performance, and safety metrics, particularly in safety-critical scenarios. Our results underscore the effectiveness of meta-learned dynamics model adaptation, advancing the development of reliable autonomous systems capable of navigating diverse and unseen environments. Video is available at:this https URL
View on arXiv@article{levy2025_2504.16923,
title={ Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving },
author={ Jacob Levy and Jason Gibson and Bogdan Vlahov and Erica Tevere and Evangelos Theodorou and David Fridovich-Keil and Patrick Spieler },
journal={arXiv preprint arXiv:2504.16923},
year={ 2025 }
}