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We propose a novel method for efficient object search in realistic environments. We formalize object search as a probabilistic inference problem over possible object locations. The method makes two contributions. First, we identify five priors, each capturing structure inherent to the physical world that is relevant to the search problem. Second, we propose a formalization of the object search problem that leverages these priors effectively. Our formalization in form of a probabilistic graphical model is capable of combining the various sources of information into a consistent probability distribution over object locations. The formalization allows us to sharpen the distribution by determining and propagating the effects of knowledge about the world. We use this reasoning method to select actions of a searching robot in a simulated environment and show that it results in efficient object search.This manuscript has been accepted at IROS 2014. Its re-submission to the ICRA 2014 workshop on "Robots in Homes and Industry" was explicitly encouraged by the workshop organizers and has been approved by the IROS 2014 program chairs.

Abstract. We investigate how video-based recognition of rat social behavior is affected by the quality of the tracking data and the derived feature set. We look at the impact of two common tracking errors -animal misidentification and inaccurate localization of body parts. We further examine how the complexity of representing the articulated body in the features influences the recognition accuracy. Our analyses show that correct identification of the rats is required to accurately recognize their interactions. Precise localization of multiple body points is beneficial for recognizing interactions that are described by a distinct pose. Including pose features only leads to improvement if the tracking algorithm can provide that data reliably.