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

TL;DR

DeComplex is a system that interprets complex natural language instructions with multiple inter-dependent tasks and generates accurate execution plans for robots, enhancing natural human-robot interaction.

Contribution

It introduces a novel method to identify task dependencies and orderings in complex instructions, addressing limitations of prior work that handled only single or independent tasks.

Findings

High accuracy in generating execution plans from complex instructions

Effective handling of task ordering and dependency detection

Improved natural language understanding for robot task execution

Abstract

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.