用自然语言+人工干预，让不同机器人在复杂地形自动识别可通行区域。
AnyTraverse: An off-road traversability framework with VLM and human operator in the loop
- 通过自然语言提示与人工介入结合，动态判断地形可通行性。
- 在3个数据集上优于现有方法，且无需重新训练模型。
- 适合需低人工干预的野外搜救、农业等场景应用。
非结构化环境下的越野通行性分割技术可支持搜救、军事、野生动物探测和农业中的自主导航。当前框架因环境差异大、场景变化不确定，且难以适配不同机器人类型而面临挑战。我们提出 AnyTraverse，结合自然语言提示与人工操作员协助，为多种机器人平台识别可通行区域。系统根据预设提示对场景进行分割，并仅在遇到未见过的景象或提示中未包含的新类别时才请求人工介入，从而降低主动监督负担，适应多变户外环境。该零样本学习方法无需大量数据收集或模型重训。实验在 RELLIS-3D、Freiburg Forest 及 RUGD 数据集上验证，结果表明其性能优于 GA-NAV 与 Off-seg，实现车辆无关的越野通行性分析，在自动化与精准人工干预间取得良好平衡。
原文摘要 · Abstract (English)
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.
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