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: https://youtu.be/cCKHHrDRQEA

Este artículo explora los viajes en el tiempo y sus implicaciones.

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