Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving

• URL: http://arxiv.org/abs/2504.16923v1
• Date: Wed, 23 Apr 2025 17:51:36 GMT
• Title: Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving
• Authors: Jacob Levy, Jason Gibson, Bogdan Vlahov, Erica Tevere, Evangelos Theodorou, David Fridovich-Keil, Patrick Spieler,
• Abstract summary: High-speed off-road autonomous driving presents unique challenges due to complex, evolving terrain characteristics.<n>We propose a novel framework that combines a Kalman filter-based online adaptation scheme with meta-learned parameters to address these challenges.<n>Our results underscore the effectiveness of meta-learned dynamics model adaptation, advancing the development of reliable autonomous systems.
• Score: 9.515695438588855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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