  A robot as a coworker or a cohabitant is becoming mainstream day-by-day with
the development of low-cost sophisticated hardware. However, an accompanying
software stack that can aid the usability of the robotic hardware remains the
bottleneck of the process, especially if the robot is not dedicated to a single
job. Programming a multi-purpose robot requires an on the fly mission
scheduling capability that involves task identification and plan generation.
The problem dimension increases if the robot accepts tasks from a human in
natural language. Though recent advances in NLP and planner development can
solve a variety of complex problems, their amalgamation for a dynamic robotic
task handler is used in a limited scope. Specifically, the problem of
formulating a planning problem from natural language instructions is not
studied in details. In this work, we provide a non-trivial method to combine an
NLP engine and a planner such that a robot can successfully identify tasks and
all the relevant parameters and generate an accurate plan for the task.
Additionally, some mechanism is required to resolve the ambiguity or missing
pieces of information in natural language instruction. Thus, we also develop a
dialogue strategy that aims to gather additional information with minimal
question-answer iterations and only when it is necessary. This work makes a
significant stride towards enabling a human-like task understanding capability
in a robot.
