In realistic applications of object search, robots will need to locate target objects in complex environments while coping with unreliable sensors, especially for small or hard-to-detect objects. In such settings, correlational information can be valuable for planning efficiently: when looking for a fork, the robot could start by locating the easier-to-detect refrigerator, since forks would probably be found nearby. Previous approaches to object search with correlational information typically resort to ad-hoc or greedy search strategies. In this paper, we propose the Correlational Object Search POMDP (COS-POMDP), which can be solved to produce search strategies that use correlational information. COS-POMDPs contain a correlation-based observation model that allows us to avoid the exponential blow-up of maintaining a joint belief about all objects, while preserving the optimal solution to this naive, exponential POMDP formulation. We propose a hierarchical planning algorithm to scale up COS-POMDP for practical domains. We conduct experiments using AI2-THOR, a realistic simulator of household environments, as well as YOLOv5, a widely-used object detector. Our results show that, particularly for hard-to-detect objects, such as scrub brush and remote control, our method offers the most robust performance compared to baselines that ignore correlations as well as a greedy, next-best view approach.
翻译:在现实的物体搜索应用中,机器人需要将目标物体定位在复杂的环境中,同时应对不可靠的传感器,特别是小型或难以探测的物体。在这样的环境下,相关信息对有效规划可能很有价值:在寻找叉子时,机器人可以首先找到较容易探测的冰箱,因为叉子可能会在附近找到。以往使用相关信息进行搜索的方法通常采用临时或贪婪的搜索战略。在本文中,我们提议使用相近对象搜索POMDP(COS-POMDP)来生成使用相关信息的搜索战略。COS-POMDP(COS-POMDP)包含基于相关信息的搜索战略。COS-POMDP(COS-POMDP)包含一个基于关联的观测模型,使我们能够避免因维持对所有物体的共同信念而发生快速爆炸,同时保留这种天真的、指数式的POMDP配方的最佳解决方案。我们提出一个等级规划算法,以扩大实际域的COS-POMDP(COS-POMDP)规模。我们使用一个现实的家庭环境模拟器,以及YOLOV5,一个广泛使用的对等对象进行搜索的搜索策略,一个最坚固的模型,我们的成果显示作为最坚固的精确的基线,作为最坚固的精确的对准的对准的对准的对准的对准的对准的对准的对准的对准的对准的对准的对准的对准的对准的对准的对准方法。我们的实验方法,我们的对准的对准的对准的对准的对准式的对准的对准的对准的对准的对准的对准式的对准的对准的对准式的对准的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式的对准式对准式的对准式的对准式