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

DeComplex: Task planning from complex natural instructions by a collocating robot