Enabling human-like task identification from natural conversation

Robots are being more and more widely used as helpers, companions, or co-workers. This means that giving instructions in unrestricted natural language is very significant as most users are non-experts. Natural language processing devices enable robots to interact with humans using natural language. However, the ambiguities of natural language make it difficult for robots to identify tasks and turn them into executable problems.

Image credit: JXGames via Pixabay, free licence

Image credit: JXGames via Pixabay, free licence

A recent paper presents a method that performs task planning from natural language instructions. In case of any ambiguities in the instruction, this system may resolve them by asking for minimal and meaningful questions. Also, the tasks which are beyond the capacity of the robot are quickly identified. The system could be able to identify correctly 95.7 % of tasks and to plan generation for 91.1 % of the total tasks.

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.