AnyTraverse: An off-road traversability framework with VLM and human operator in the loop
This addresses the problem of adaptive navigation in off-road settings for applications like search-and-rescue and agriculture, offering a vehicle-agnostic solution with reduced human supervision, though it appears incremental as it builds on existing methods with human-in-the-loop enhancements.
The paper tackles off-road traversability segmentation for autonomous navigation by introducing AnyTraverse, a framework that combines natural language prompts with human-operator assistance to adapt to unstructured environments and diverse robots, achieving better performance than GA-NAV and Off-seg in experiments on datasets like RELLIS-3D.
Off-road traversability segmentation enables autonomous navigation with applications in search-and-rescue, military operations, wildlife exploration, and agriculture. Current frameworks struggle due to significant variations in unstructured environments and uncertain scene changes, and are not adaptive to be used for different robot types. We present AnyTraverse, a framework combining natural language-based prompts with human-operator assistance to determine navigable regions for diverse robotic vehicles. The system segments scenes for a given set of prompts and calls the operator only when encountering previously unexplored scenery or unknown class not part of the prompt in its region-of-interest, thus reducing active supervision load while adapting to varying outdoor scenes. Our zero-shot learning approach eliminates the need for extensive data collection or retraining. Our experimental validation includes testing on RELLIS-3D, Freiburg Forest, and RUGD datasets and demonstrate real-world deployment on multiple robot platforms. The results show that AnyTraverse performs better than GA-NAV and Off-seg while offering a vehicle-agnostic approach to off-road traversability that balances automation with targeted human supervision.