Interactive Learning of State Representation through Natural Language
  Instruction and Explanation
  One significant simplification in most previous work on robot learning is the
closed-world assumption where the robot is assumed to know ahead of time a
complete set of predicates describing the state of the physical world. However,
robots are not likely to have a complete model of the world especially when
learning a new task. To address this problem, this extended abstract gives a
brief introduction to our on-going work that aims to enable the robot to
acquire new state representations through language communication with humans.
