  As the number of robots in our daily surroundings like home, office,
restaurants, factory floors, etc. are increasing rapidly, the development of
natural human-robot interaction mechanism becomes more vital as it dictates the
usability and acceptability of the robots. One of the valued features of such a
cohabitant robot is that it performs tasks that are instructed in natural
language. However, it is not trivial to execute the human intended tasks as
natural language expressions can have large linguistic variations. Existing
works assume either single task instruction is given to the robot at a time or
there are multiple independent tasks in an instruction. However, complex task
instructions composed of multiple inter-dependent tasks are not handled
efficiently in the literature. There can be ordering dependency among the
tasks, i.e., the tasks have to be executed in a certain order or there can be
execution dependency, i.e., input parameter or execution of a task depends on
the outcome of another task. Understanding such dependencies in a complex
instruction is not trivial if an unconstrained natural language is allowed. In
this work, we propose a method to find the intended order of execution of
multiple inter-dependent tasks given in natural language instruction. Based on
our experiment, we show that our system is very accurate in generating a viable
execution plan from a complex instruction.
