MAMMOTH: Multi-Modal End-to-End Policy for Robust Off-Road Navigation

Jul 15, 2026

Researchers present MAMMOTH, a unified end-to-end navigation policy that integrates RGB, thermal, 3D point cloud, and ego velocity data for autonomous off-road navigation. The system employs a modality dropout training scheme to ensure robustness to missing sensor inputs and uses a diffusion policy for safer, terrain-aware trajectory planning. Real-world experiments demonstrate improved collision avoidance, terrain-aware planning, and generalization to missing modalities, including during night-time operation.

Why it matters: This work advances autonomous off-road navigation by enabling robust performance even when some sensors fail or degrade, addressing a key challenge in the field.

Full story at: arXiv Robotics ↗