AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915687208845312 |
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| author | Fu, Taimeng Zhan, Zitong Zhao, Zhipeng Du, Yi Su, Shaoshu Lin, Xiao Esfahani, Ehsan Tarkesh Dantu, Karthik Chowdhury, Souma Wang, Chen |
| author_facet | Fu, Taimeng Zhan, Zitong Zhao, Zhipeng Du, Yi Su, Shaoshu Lin, Xiao Esfahani, Ehsan Tarkesh Dantu, Karthik Chowdhury, Souma Wang, Chen |
| contents | Off-road navigation is critical for a wide range of field robotics applications from planetary exploration to disaster response. However, it remains a longstanding challenge due to unstructured environments and the inherently complex terrain-vehicle interactions. Traditional physics-based methods struggle to accurately capture the nonlinear dynamics underlying these interactions, while purely data-driven approaches often overfit to specific motion patterns, vehicle geometries, or platforms, limiting their generalization in diverse, real-world scenarios. To address these limitations, we introduce AnyNav, a vision-based friction estimation and navigation framework grounded in neuro-symbolic principles. Our approach integrates neural networks for visual perception with symbolic physical models for reasoning about terrain-vehicle dynamics. To enable self-supervised learning in real-world settings, we adopt the imperative learning paradigm, employing bilevel optimization to train the friction network through physics-based optimization. This explicit incorporation of physical reasoning substantially enhances generalization across terrains, vehicle types, and operational conditions. Leveraging the predicted friction coefficients, we further develop a physics-informed navigation system capable of generating physically feasible, time-efficient paths together with corresponding speed profiles. We demonstrate that AnyNav seamlessly transfers from simulation to real-world robotic platforms, exhibiting strong robustness across different four-wheeled vehicles and diverse off-road environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12654 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation Fu, Taimeng Zhan, Zitong Zhao, Zhipeng Du, Yi Su, Shaoshu Lin, Xiao Esfahani, Ehsan Tarkesh Dantu, Karthik Chowdhury, Souma Wang, Chen Robotics Off-road navigation is critical for a wide range of field robotics applications from planetary exploration to disaster response. However, it remains a longstanding challenge due to unstructured environments and the inherently complex terrain-vehicle interactions. Traditional physics-based methods struggle to accurately capture the nonlinear dynamics underlying these interactions, while purely data-driven approaches often overfit to specific motion patterns, vehicle geometries, or platforms, limiting their generalization in diverse, real-world scenarios. To address these limitations, we introduce AnyNav, a vision-based friction estimation and navigation framework grounded in neuro-symbolic principles. Our approach integrates neural networks for visual perception with symbolic physical models for reasoning about terrain-vehicle dynamics. To enable self-supervised learning in real-world settings, we adopt the imperative learning paradigm, employing bilevel optimization to train the friction network through physics-based optimization. This explicit incorporation of physical reasoning substantially enhances generalization across terrains, vehicle types, and operational conditions. Leveraging the predicted friction coefficients, we further develop a physics-informed navigation system capable of generating physically feasible, time-efficient paths together with corresponding speed profiles. We demonstrate that AnyNav seamlessly transfers from simulation to real-world robotic platforms, exhibiting strong robustness across different four-wheeled vehicles and diverse off-road environments. |
| title | AnyNav: Visual Neuro-Symbolic Friction Learning for Off-road Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2501.12654 |