  Robotic task instructions often involve a referred object that the robot must
locate (ground) within the environment. While task intent understanding is an
essential part of natural language understanding, less effort is made to
resolve ambiguity that may arise while grounding the task. Existing works use
vision-based task grounding and ambiguity detection, suitable for a fixed view
and a static robot. However, the problem magnifies for a mobile robot, where
the ideal view is not known beforehand. Moreover, a single view may not be
sufficient to locate all the object instances in the given area, which leads to
inaccurate ambiguity detection. Human intervention is helpful only if the robot
can convey the kind of ambiguity it is facing. In this article, we present DoRO
(Disambiguation of Referred Object), a system that can help an embodied agent
to disambiguate the referred object by raising a suitable query whenever
required. Given an area where the intended object is, DoRO finds all the
instances of the object by aggregating observations from multiple views while
exploring & scanning the area. It then raises a suitable query using the
information from the grounded object instances. Experiments conducted with the
AI2Thor simulator show that DoRO not only detects the ambiguity more accurately
but also raises verbose queries with more accurate information from the
visual-language grounding.
