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For mobile robots to communicate meaningfully about their spatial environment, they require personally constructed cognitive maps and social interactions to form languages with shared meanings. Geographic spatial concepts introduce particular problems for grounding—connecting a word to its referent in the world—because such concepts cannot be directly and solely based on sensory perceptions. In this article we investigate the grounding of geographic spatial concepts using mobile robots with cognitive maps, called Lingodroids. Languages were established through structured interactions between pairs of robots called where-are-we conversations. The robots used a novel method, termed the distributed lexicon table, to create flexible concepts. This method enabled words for locations, termed toponyms, to be grounded through experience. Their understanding of the meaning of words was demonstrated using go-to games in which the robots independently navigated to named locations. Studies in real and virtual reality worlds show that the system is effective at learning spatial language: robots learn words easily—in a single trial as children do—and the words and their meaning are sufficiently robust for use in real world tasks.

26:T434,

The agent-based model presented here builds on existing models, allowing for multiple partnerships, in