A POMDP-based Robot-Human Trust Model for Human-Robot Collaboration

Pallavi Tilloo, Jesse Parron, Omar Obidat, Michelle Zhu, Weitian Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Trust is a cognitive ability that can be dependent on behavioral consistency. In this paper, a partially observable Markov Decision Process (POMDP)-based computational robot-human trust model is proposed for hand-over tasks in human-robot collaborative contexts. The robot's trust in its human partner is evaluated based on the human behavior estimates and object detection during the hand-over task. The human-robot hand-over process is parameterized as a partially observable Markov Decision Process. The proposed approach is verified in real-world human-robot collaborative tasks. Results show that our approach can be successfully applied to human-robot hand-over tasks to achieve high efficiency, reduce redundant robot movements, and realize predictability and mutual understanding of the task.

Original languageEnglish
Title of host publication2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1009-1014
Number of pages6
ISBN (Electronic)9781665472678
DOIs
StatePublished - 2022
Event12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022 - Baishan, China
Duration: 27 Jul 202231 Jul 2022

Publication series

Name2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

Conference

Conference12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022
Country/TerritoryChina
CityBaishan
Period27/07/2231/07/22

Keywords

  • POMDP
  • belief probability
  • human-robot collaboration
  • robotics
  • trust

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