Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915255300390912 |
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| author | Levy, Jacob Gibson, Jason Vlahov, Bogdan Tevere, Erica Theodorou, Evangelos Fridovich-Keil, David Spieler, Patrick |
| author_facet | Levy, Jacob Gibson, Jason Vlahov, Bogdan Tevere, Erica Theodorou, Evangelos Fridovich-Keil, David Spieler, Patrick |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving Levy, Jacob Gibson, Jason Vlahov, Bogdan Tevere, Erica Theodorou, Evangelos Fridovich-Keil, David Spieler, Patrick Robotics Machine Learning Systems and Control 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 |
| title | Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving |
| topic | Robotics Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2504.16923 |