In this paper, we consider the problem of how background knowledge about usual object arrangements can be utilized by a mobile robot to more efficiently find an object in an unknown environment. We decompose the action selection problem during the search into two parts. First, we compute a belief over the location of the object and subsequently use the belief to select the next target location the robot should visit. For the inference part, we utilize a maximum entropy model which models the conditional distribution over possible locations of the target object given the observations made so far. The model is based on co-occurrences of objects and object attributes in different spatial contexts. The parameters are learned by maximizing the data likelihood using gradient ascent. We evaluate our approach by simulated search runs based on data obtained from different real-world environments. Our results show a significant improvement over a standard search technique which does not employ domain-specific background knowledge.
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