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In partially observed environments, it can be useful for a human to providethe robot with declarative information that represents probabilistic relationalconstraints on properties of objects in the world, augmenting the robot'ssensory observations. For instance, a robot tasked with a search-and-rescuemission may be informed by the human that two victims are probably in the sameroom. An important question arises: how should we represent the robot'sinternal knowledge so that this information is correctly processed and combinedwith raw sensory information? In this paper, we provide an efficient beliefstate representation that dynamically selects an appropriate factoring,combining aspects of the belief when they are correlated through informationand separating them when they are not. This strategy works in open domains, inwhich the set of possible objects is not known in advance, and providessignificant improvements in inference time over a static factoring, leading tomore efficient planning for complex partially observed tasks. We validate ourapproach experimentally in two open-domain planning problems: a 2D discretegridworld task and a 3D continuous cooking task. A supplementary video can befound at http://tinyurl.com/chitnis-iros-18.

Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization