Automatic Trust Estimation From Movement Data in Industrial Human-Robot Collaboration Based on Deep Learning

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Trust in automation is usually assessed with post-interaction questionnaires. For human robot collaboration it would be beneficial to assess the trust level during the interaction to adjust the robot's collaboration behavior to the user expectations. In this paper we investigate if trust can be estimated from observable behavior like movements during the interaction with a large industrial manipulator. To this end, we report on a data collection for two tasks during collaborative draping, the transport of large cut pieces and the actual draping process in close proximity to the robot. The data is used to train and compare different deep learning models. Results show that automatic trust estimation is feasible, which opens up to using trust as a parameter for informing the interaction with robots.
Original languageEnglish
Title of host publicationProceedings of ICRA 2024
Number of pages6
PublisherIEEE
Publication statusAccepted/In press - 2024
Event2024 IEEE International Conference on Robotics and Automation - Pacifico, Yokohama, Japan
Duration: 13 May 202417 May 2024
https://2024.ieee-icra.org/

Conference

Conference2024 IEEE International Conference on Robotics and Automation
LocationPacifico
Country/TerritoryJapan
CityYokohama
Period13/05/202417/05/2024
Internet address

Keywords

  • human robot trust
  • Deep Learning
  • Human Robot Collaboration

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