Probabilistic Active Filtering For Object Search In Clutter

This paper proposes a probabilistic approach for object search in clutter. Due to heavy occlusions, it is vital for an agent to be able to gradually reduce uncertainty in observations of the objects in its workspace by systematically rearranging them. Probabilistic methodologies present a promising sample-efficient alternative to handle the massively complex state-action space that inherently comes with this problem, avoiding the need for both exhaustive training samples and the accompanying heuristics for traversing a large-scale model during runtime. We approach the object search problem by extending a Gaussian Process active filtering strategy with an additional model for capturing state dynamics as the objects are moved over the course of the activity. This allows viable models to be built upon relatively scarce training data, while the complexity of the action space is also reduced by shifting objects over relatively short distances. Validation in both simulation and with a real Baxter robot with a limited number of training samples demonstrates the efficacy of the proposed approach.