无需传感器模型，也能在未知环境里安全规划路径。
Safe Planning in Unknown Environments Using Conformalized Semantic Maps
- 用置信预测动态构建语义地图，自动量化感知不确定性
- 保证用户设定的成功率，实验中任务成功率显著高于基线
- 适合对安全性要求高的机器人导航场景
本文解决未知环境中存在感知不确定性下的语义规划问题。环境包含多个未知的语义标签区域或物体，机器人需到达目标位置，同时与不同类别保持指定距离。目标是计算出在感知不确定下仍能以用户定义概率完成任务的路径。现有方法要么忽略感知不确定性，缺乏正确性保证；要么依赖已知传感器模型和噪声特性。本文首次提出无需任何传感器模型或噪声知识即可实现用户指定任务完成率的语义避障规划器。通过在在线感知测量上构建语义地图，并使用无模型、无分布假设的置信预测量化不确定性，实现了理论保证的任务成功率。大量实验验证了该方法的有效性，其任务成功率始终优于基线方法。
原文摘要 · Abstract (English)
This paper addresses semantic planning problems in unknown environments under perceptual uncertainty. The environment contains multiple unknown semantically labeled regions or objects, and the robot must reach desired locations while maintaining class-dependent distances from them. We aim to compute robot paths that complete such semantic reach-avoid tasks with user-defined probability despite uncertain perception. Existing planning algorithms either ignore perceptual uncertainty, thus lacking correctness guarantees, or assume known sensor models and noise characteristics. In contrast, we present the first planner for semantic reach-avoid tasks that achieves user-specified mission completion rates without requiring any knowledge of sensor models or noise. This is enabled by quantifying uncertainty in semantic maps, constructed on-the-fly from perceptual measurements, using conformal prediction in a model and distribution free manner. We validate our approach and the theoretical mission completion rates through extensive experiments, showing that it consistently outperforms baselines in mission success rates.
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