Title: Efficient Manipulation-Enhanced Semantic Mapping With Uncertainty-Informed Action Selection Show Chinese title

Title: 基于不确定性感知动作选择的高效操作增强语义地图构建

Abstract: Service robots operating in cluttered human environments such as homes, offices, and schools cannot rely on predefined object arrangements and must continuously update their semantic and spatial estimates while dealing with possible frequent rearrangements. Efficient and accurate mapping under such conditions demands selecting informative viewpoints and targeted manipulations to reduce occlusions and uncertainty. In this work, we present a manipulation-enhanced semantic mapping framework for occlusion-heavy shelf scenes that integrates evidential metric-semantic mapping with reinforcement-learning-based next-best view planning and targeted action selection. Our method thereby exploits uncertainty estimates from Dirichlet and Beta distributions in the map prediction networks to guide both active sensor placement and object manipulation, focusing on areas with high uncertainty and selecting actions with high expected information gain. Furthermore, we introduce an uncertainty-informed push strategy that targets occlusion-critical objects and generates minimally invasive actions to reveal hidden regions by reducing overall uncertainty in the scene. The experimental evaluation shows that our framework enables to accurately map cluttered scenes, while substantially reducing object displacement and achieving a 95% reduction in planning time compared to the state-of-the-art, thereby realizing real-world applicability. Show Chinese abstract

Abstract: 服务机器人在杂乱的人类环境（如家庭、办公室和学校）中运行，不能依赖预定义的物体排列，必须在处理可能频繁重新排列的情况下持续更新其语义和空间估计。 在这些条件下，高效且准确的映射需要选择有信息量的视角和针对性的操作，以减少遮挡和不确定性。 在本工作中，我们提出了一种增强操作的语义映射框架，用于遮挡密集的书架场景，该框架将证据度量-语义映射与基于强化学习的下一个最佳视角规划和目标动作选择相结合。 我们的方法因此利用地图预测网络中的Dirichlet和Beta分布的不确定性估计，以指导主动传感器布置和物体操作，重点关注高不确定性区域，并选择具有高期望信息增益的动作。 此外，我们引入了一种基于不确定性的推动策略，针对遮挡关键物体，并生成最小侵入性动作，通过减少场景中的总体不确定性来揭示隐藏区域。 实验评估表明，我们的框架能够准确映射杂乱场景，同时显著减少物体位移，并相比最先进的方法实现了95%的规划时间减少，从而实现了现实应用。