  Many task domains require robots to interpret and act upon natural language
commands which are given by people and which refer to the robot's physical
surroundings. Such interpretation is known variously as the symbol grounding
problem, grounded semantics and grounded language acquisition. This problem is
challenging because people employ diverse vocabulary and grammar, and because
robots have substantial uncertainty about the nature and contents of their
surroundings, making it difficult to associate the constitutive language
elements (principally noun phrases and spatial relations) of the command text
to elements of those surroundings. Symbolic models capture linguistic structure
but have not scaled successfully to handle the diverse language produced by
untrained users. Existing statistical approaches can better handle diversity,
but have not to date modeled complex linguistic structure, limiting achievable
accuracy. Recent hybrid approaches have addressed limitations in scaling and
complexity, but have not effectively associated linguistic and perceptual
features. Our framework, called Generalized Grounding Graphs (G^3), addresses
these issues by defining a probabilistic graphical model dynamically according
to the linguistic parse structure of a natural language command. This approach
scales effectively, handles linguistic diversity, and enables the system to
associate parts of a command with the specific objects, places, and events in
the external world to which they refer. We show that robots can learn word
meanings and use those learned meanings to robustly follow natural language
commands produced by untrained users. We demonstrate our approach for both
mobility commands and mobile manipulation commands involving a variety of
semi-autonomous robotic platforms, including a wheelchair, a micro-air vehicle,
a forklift, and the Willow Garage PR2.
