Document Type
Conference Proceeding
Publication Date
1-1-2023
Journal / Book Title
Proceedings of 13th IEEE International Conference on Cyber Technology in Automation Control and Intelligent Systems Cyber 2023
Abstract
Human-robot collaboration has been one of the main focuses for both research and usage in advanced manufacturing. In human-robot partnerships, instead of static collaboration for repetitive tasks, it is more significant for the robot to dynamically understand its human partner's intentions and collaborate with them to complete the shared tasks. Motivated by these issues, we develop a model for the robot to learn to complete tasks by watching and analyzing human demonstrations. This allows the robot to become more accurate and customizable with each human's personalized working preference. Based on the long short-term memory method, we propose a new approach to have the robot recognize objects, understand ongoing human actions, and predict human intentions. This will allow the robot to automatically adjust its motions and dynamically pick up and deliver the object to its human partner in the collaborative task. Experimental results suggest that the proposed model can enable robots, like humans, to learn and predict humans' intentions dynamically and intelligently to accommodate customized and personalized collaborative tasks. Future work of this study is also discussed.
DOI
10.1109/CYBER59472.2023.10256441
Journal ISSN / Book ISBN
85174689983 (Scopus)
Montclair State University Digital Commons Citation
Obidat, Omar; Parron, Jesse; Li, Rui; Rodano, Julia; and Wang, Weitian, "Development of a Teaching-Learning-Prediction-Collaboration Model for Human-Robot Collaborative Tasks" (2023). School of Computing Faculty Scholarship and Creative Works. 50.
https://digitalcommons.montclair.edu/computing-facpubs/50