Learning search heuristics for finding objects in structured environments

We consider the problem of efficiently finding an object with a mobile robot in an initially unknown, structured environment. The overall goal is to allow the robot to improve upon a standard exploration technique by utilizing background knowledge from previously seen, similar environments. We present two conceptually different approaches. Whereas the first method, which is the focus of this article, is a reactive search technique that decides where to search next only based on local information about the objects in the robot's vicinity, the second algorithm is a more global and inference-based approach that explicitly reasons about the location of the target object given all observations made so far. While the model underlying the first approach can be learned from data of optimal search paths, we learn the model of the second method from object arrangements of example environments. Our application scenario is the search for a product in a supermarket. We present simulation and real-world experiments in which we compare our strategies to alternative methods and also to the performance of humans.