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.

翻译：本文研究在未知环境中存在感知不确定性时的语义规划问题。环境包含多个未知的语义标记区域或对象，机器人需在抵达目标位置的同时保持与这些区域或对象的类别相关距离。我们的目标是在感知不确定的情况下，计算能以用户定义概率完成此类语义到达-规避任务的机器人路径。现有规划算法要么忽略感知不确定性而缺乏正确性保证，要么假设已知传感器模型与噪声特性。与此不同，我们提出了首个针对语义到达-规避任务的规划器，该规划器无需任何传感器模型或噪声的先验知识即可实现用户指定的任务完成率。这一成果通过采用保形预测方法，以模型无关和分布无关的方式，对基于实时感知测量构建的语义地图进行不确定性量化而实现。我们通过大量实验验证了所提方法及理论任务完成率，结果表明该方法在任务成功率方面持续优于基线模型。