让人类实时标注，小样本快速提升机器人行进能力判断
Do You Know the Way? Human-in-the-Loop Understanding for Fast Traversability Estimation in Mobile Robotics
- 人类按需标注，模型基于基础模型快速学习新数据
- 仅需少量标注即可达到顶尖预测性能
- 适合需要快速适应新环境的野外机器人任务
移动机器人在非结构化环境中执行任务时，需准确判断可通行区域。现有几何方法难以捕捉复杂通行性特征，视觉方法则常依赖大量人工标注或机器人经验，且部署中无法适应领域变化。为此，我们提出一种人机协同（HiL）的通行性估计方法：按需向人类请求标注，利用基础模型实现对新标注数据的快速学习，并在少量快速标注下提供高精度预测。我们在仿真和真实数据上广泛验证，结果表明该方法性能达当前最优水平。
原文摘要 · Abstract (English)
The increasing use of robots in unstructured environments necessitates the development of effective perception and navigation strategies to enable field robots to successfully perform their tasks. In particular, it is key for such robots to understand where in their environment they can and cannot travel -- a task known as traversability estimation. However, existing geometric approaches to traversability estimation may fail to capture nuanced representations of traversability, whereas vision-based approaches typically either involve manually annotating a large number of images or require robot experience. In addition, existing methods can struggle to address domain shifts as they typically do not learn during deployment. To this end, we propose a human-in-the-loop (HiL) method for traversability estimation that prompts a human for annotations as-needed. Our method uses a foundation model to enable rapid learning on new annotations and to provide accurate predictions even when trained on a small number of quickly-provided HiL annotations. We extensively validate our method in simulation and on real-world data, and demonstrate that it can provide state-of-the-art traversability prediction performance.
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