Natural Language and Robotics
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 generate 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.