不确定性感知的开放词汇目标导航主动感知方法
Uncertainty-Informed Active Perception for Open Vocabulary Object Goal Navigation
AI总结：
针对目标导航中提示措辞引起的语义不确定性问题，提出不确定性感知的主动感知流程，利用概率传感器模型和不确定性感知多臂老虎机规划器，实现与最先进方法相当的成功率。
摘要：
在室内环境中移动的机器人越来越依赖视觉语言模型来感知相机图像中的高层语义线索，如物体类别。这类模型有望大幅提升机器人在诸如目标导航（ObjectNav）等任务中的行为表现，在该任务中，机器人必须通过探索环境来定位自然语言指定的物体。当前的目标导航方法严重依赖针对感知的提示工程，且未处理由提示措辞变化引起的语义不确定性。忽略语义不确定性可能导致次优的探索，进而限制性能。因此，我们提出了一种面向室内环境目标导航的语义不确定性感知主动感知流程。我们引入了一种新颖的概率传感器模型，用于量化视觉语言模型中的语义不确定性，并将其整合到概率几何-语义地图中，以增强空间理解。基于该地图，我们开发了一种前沿探索规划器，采用不确定性感知的多臂老虎机目标来引导高效的物体搜索。实验结果表明，我们的方法在不需大量提示工程的情况下，达到了与最先进方法相当的目标导航成功率。
英文摘要：
Mobile robots exploring indoor environments increasingly rely on vision-language models to perceive high-level semantic cues in camera images, such as object categories. Such models offer the potential to substantially advance robot behaviour for tasks such as object-goal navigation (ObjectNav), where the robot must locate objects specified in natural language by exploring the environment. Current ObjectNav methods heavily depend on prompt engineering for perception and do not address the semantic uncertainty induced by variations in prompt phrasing. Ignoring semantic uncertainty can lead to suboptimal exploration, which in turn limits performance. Hence, we propose a semantic uncertainty-informed active perception pipeline for ObjectNav in indoor environments. We introduce a novel probabilistic sensor model for quantifying semantic uncertainty in vision-language models and incorporate it into a probabilistic geometric-semantic map to enhance spatial understanding. Based on this map, we develop a frontier exploration planner with an uncertainty-informed multi-armed bandit objective to guide efficient object search. Experimental results demonstrate that our method achieves ObjectNav success rates comparable to those of state-of-the-art approaches, without requiring extensive prompt engineering.
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