In order for robots to engage in dialog with human teammates, they must have the ability to identify correspondences between elements of language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990 ) would enable a robot to interpret commands such as "Drive over to receiving and pick up the tire pallet." This article describes several of our results that use probabilistic inference to address the symbol grounding problem. Our approach is to develop models that factor according to the linguistic structure of a command. We first describe an early result, a generative model that factors according to the sequential structure of language, then discuss our new framework, Generalized Grounding Graphs (G 3 ). The G 3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments in which the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation.
In REF , the authors address natural language instructions about motion and grasping, that are mapped into Generalized Grounding Graphs (G 3 ).
Approaching the Symbol Grounding Problem with Probabilistic Graphical Models
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