Learning When to Jump for Off-road Navigation

• URL: http://arxiv.org/abs/2602.00877v1
• Date: Sat, 31 Jan 2026 19:41:09 GMT
• Title: Learning When to Jump for Off-road Navigation
• Abstract summary: We introduce Motion-aware Traversability representation to explicitly model terrain cost conditioned on actual robot motion.<n>We develop a system that integrates MAT to enable agile off-road navigation.<n>Results show that MAT achieves real-time efficiency and enhances the performance of off-road navigation.
• Score: 10.997518969388084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low speed does not always guarantee safety in off-road driving. For instance, crossing a ditch may be risky at a low speed due to the risk of getting stuck, yet safe at a higher speed with a controlled, accelerated jump. Achieving such behavior requires path planning that explicitly models complex motion dynamics, whereas existing methods often neglect this aspect and plan solely based on positions or a fixed velocity. To address this gap, we introduce Motion-aware Traversability (MAT) representation to explicitly model terrain cost conditioned on actual robot motion. Instead of assigning a single scalar score for traversability, MAT models each terrain region as a Gaussian function of velocity. During online planning, we decompose the terrain cost computation into two stages: (1) predict terrain-dependent Gaussian parameters from perception in a single forward pass, (2) efficiently update terrain costs for new velocities inferred from current dynamics by evaluating these functions without repeated inference. We develop a system that integrates MAT to enable agile off-road navigation and evaluate it in both simulated and real-world environments with various obstacles. Results show that MAT achieves real-time efficiency and enhances the performance of off-road navigation, reducing path detours by 75% while maintaining safety across challenging terrains.

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