Abstract: Natural language can be a powerful, flexible way for people to interact with robots. A particular challenge for designers of embodied robots, in contrast to disembodied methods such as
phone-based information systems, is that natural language
understanding systems must map between linguistic elements and aspects
of the external world, thereby solving the so-called symbol grounding problem. This talk describes a probabilistic framework for robust interpretation of grounded natural language, called Generalized Grounding Graphs (G^3). The G^3 framework leverages the structure of
language to define a probabilistic graphical model that maps between elements in the language and aspects of the external world. It can
compose learned word meanings to understand novel commands that may have never been seen during training. Taking a probabilistic approach
enables the robot to employ information-theoretic dialog strategies,
asking targeted questions to reduce uncertainty about different parts
of a natural language command. By inverting the model, the robot can generated targeted natural language requests for help from a human
partner. This approach points the way toward more general models of grounded language understanding, which will lead to robots capable of
building world models from both linguistic and non-linguistic input,
following complex grounded natural language commands, and engaging in
fluid, flexible dialog with their human partners.
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