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As robots become more ubiquitous and capable, it becomes ever more importantto enable untrained users to easily interact with them. Recently, this has ledto study of the language grounding problem, where the goal is to extractrepresentations of the meanings of natural language tied to perception andactuation in the physical world. In this paper, we present an approach forjoint learning of language and perception models for grounded attributeinduction. Our perception model includes attribute classifiers, for example todetect object color and shape, and the language model is based on aprobabilistic categorial grammar that enables the construction of rich,compositional meaning representations. The approach is evaluated on the task ofinterpreting sentences that describe sets of objects in a physical workspace.We demonstrate accurate task performance and effective latent-variable conceptinduction in physical grounded scenes.

A Joint Model of Language and Perception for Grounded Attribute Learning