Following Directions Using Statistical Machine Translation
Human-Robot Interaction, 2010
Abstract
Mobile robots that interact with humans in an
intuitive way must be able to follow directions provided by
humans in unconstrained natural language. In this work we
investigate how statistical machine translation techniques can
be used to bridge the gap between natural language route
instructions and a map of an environment built by a robot.
Our approach uses training data to learn to translate from
natural language instructions to an automatically-labeled map.
The complexity of the translation process is controlled by taking
advantage of physical constraints imposed by the map. As a
result, our technique can efficiently handle uncertainty in both
map labeling and parsing. Our experiments demonstrate the
promising capabilities achieved by our approach.