  The number of robots deployed in our daily surroundings is ever-increasing.
Even in the industrial set-up, the use of coworker robots is increasing
rapidly. These cohabitant robots perform various tasks as instructed by
co-located human beings. Thus, a natural interaction mechanism plays a big role
in the usability and acceptability of the robot, especially by a non-expert
user. The recent development in natural language processing (NLP) has paved the
way for chatbots to generate an automatic response for users' query. A robot
can be equipped with such a dialogue system. However, the goal of human-robot
interaction is not focused on generating a response to queries, but it often
involves performing some tasks in the physical world. Thus, a system is
required that can detect user intended task from the natural instruction along
with the set of pre- and post-conditions. In this work, we develop a dialogue
engine for a robot that can classify and map a task instruction to the robot's
capability. If there is some ambiguity in the instructions or some required
information is missing, which is often the case in natural conversation, it
asks an appropriate question(s) to resolve it. The goal is to generate minimal
and pin-pointed queries for the user to resolve an ambiguity. We evaluate our
system for a telepresence scenario where a remote user instructs the robot for
various tasks. Our study based on 12 individuals shows that the proposed
dialogue strategy can help a novice user to effectively interact with a robot,
leading to satisfactory user experience.
