Hierarchical Intention Tracking for Robust Human-Robot Collaboration in Industrial Assembly Tasks

Zhe Huang, Ye Ji Mun, Xiang Li, Yiqing Xie, Ninghan Zhong, Weihang Liang, Junyi Geng, Tan Chen, Katherine Driggs-Campbell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Collaborative robots require effective human intention estimation to safely and smoothly work with humans in less structured tasks such as industrial assembly, where human intention continuously changes. We propose the concept of intention tracking and introduce a collaborative robot system that concurrently tracks intentions at hierarchical levels. The high-level intention is tracked to estimate human's interaction pattern and enable robot to (1) avoid collision with human to minimize interruption and (2) assist human to correct failure. The low-level intention estimate provides robot with task-related information. We implement the system on a UR5e robot and demonstrate robust, seamless and ergonomic human-robot collaboration in an ablative pilot study of an assembly use case.

Original languageEnglish (US)
Title of host publicationProceedings - ICRA 2023
Subtitle of host publicationIEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9821-9828
Number of pages8
ISBN (Electronic)9798350323658
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom
Duration: May 29 2023Jun 2 2023

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2023-May
ISSN (Print)1050-4729

Conference

Conference2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Country/TerritoryUnited Kingdom
CityLondon
Period5/29/236/2/23

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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