Human-robot interactions (HRI) in natural language  pose many challenges for natural language understanding (NLU) systems, for humans expect robots to (1) generate quick responses to their request, which requires all processing to be done in real-time, (2) to rapidly integrate perceptions (e.g., to resolve referents (Brick and Scheutz, 2007)), and (3) to provide backchannel feedback indicating whether they understood an instruction, often before the end of an utterance.
nLU systems on robots must operate incrementally to allow for the construction of meaning that can lead to robot action before an utterance is completed (e.g., a head-turn of the robot to check for an object referred to by the speaker).