Joint BERT Model for Intent Classification and Slot Filling Analysis of Natural Language Instructions in Co-Robotic Field Construction Work

Abstract

As the construction industry faces the challenges of a worker shortage and low productivity rate, there is a growing interest in using human-robot collaboration (HRC) in construction. HRC allows for a combination of the accuracy and repeatability of robots with the flexibility and intelligence of human workers. To take advantage of its potential benefits, it is important for construction workers who are not robotic experts to interact easily with robots through intuitive and natural user interfaces. Even though many studies have been performed on HRC using natural language, little research has been conducted on this topic in construction. This paper conducts natural language understanding of language instructions for pick-and-place operations in construction using the language model Joint BERT. Experimental results show high accuracy on intent classification and slot filling tasks, allowing the robot to perform tasks accurately for a given natural language instruction.