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Trust, but Verify: Autonomous Robot Trust Modeling in Human-Robot Collaboration

Published:09 November 2021Publication History

ABSTRACT

In this work, we propose a computational robot trust model based on the predictability of the human partner as the main factor that impacts robot trust. Using this model and based on the robot’s knowledge about the task, the robot switches its behavior between conservative and normal, and adjusts the implemented safety mechanism as a function of the current robot trust level. We illustrate the proposed model impact on the outcome of the collaboration using a simple scenario. The results show a significant improvement in safety conditions of the human in collaboration with a robot at the cost of justifiable team performance reduction.

References

  1. Basel Alhaji, Janine Beecken, Rüdiger Ehlers, Jan Gertheiss, Felix Merz, Jörg P. Müller, Michael Prilla, Andreas Rausch, Andreas Reinhardt, Delphine Reinhardt, Christian Rembe, Niels-Ole Rohweder, Christoph Schwindt, Stephan Westphal, and Jürgen Zimmermann. 2020. Engineering Human–Machine Teams for Trusted Collaboration. Big Data and Cognitive Computing 4, 4 (Dec. 2020), 35. https://doi.org/10.3390/bdcc4040035Google ScholarGoogle ScholarCross RefCross Ref
  2. Basel Alhaji, Michael Prilla, and Andreas Rausch. 2021. Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration. Frontiers in Artificial Intelligence 4 (2021). https://doi.org/10.3389/frai.2021.703504 Publisher: Frontiers.Google ScholarGoogle ScholarCross RefCross Ref
  3. Mehrnoosh Askarpour, Dino Mandrioli, Matteo Rossi, and Federico Vicentini. 2019. Formal model of human erroneous behavior for safety analysis in collaborative robotics. Robotics and Computer-Integrated Manufacturing 57 (June 2019), 465–476. https://doi.org/10.1016/j.rcim.2019.01.001Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. David J Atkinson, William J Clancey, and Micah H Clark. 2014. Shared Awareness, Autonomy and Trust in Human-Robot Teamwork. In AAAI Fall Symposia. https://www.aaai.org/ocs/index.php/FSS/FSS14/paper/view/9146Google ScholarGoogle Scholar
  5. František Duchoň, Dominik Huňady, Martin Dekan, and Andrej Babinec. 2013. Optimal Navigation for Mobile Robot in Known Environment. Applied Mechanics and Materials 282 (Jan. 2013), 33–38. https://doi.org/10.4028/www.scientific.net/AMM.282.33Google ScholarGoogle ScholarCross RefCross Ref
  6. Ion Juvina, Michael G. Collins, Othalia Larue, William G. Kennedy, Ewart De Visser, and Celso De Melo. 2019. Toward a Unified Theory of Learned Trust in Interpersonal and Human-Machine Interactions. ACM Transactions on Interactive Intelligent Systems 9, 4 (Dec. 2019), 1–33. https://doi.org/10.1145/3230735Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. John D Lee and Katrina A See. 2004. Trust in Automation: Designing for Appropriate Reliance. Human Factors (2004), 31. https://journals.sagepub.com/doi/10.1518/hfes.46.1.50_30392Google ScholarGoogle Scholar
  8. David V. Lu and William D. Smart. 2011. Human-robot interactions as theatre. In 2011 RO-MAN. IEEE, Atlanta, GA, USA, 473–478. https://doi.org/10.1109/ROMAN.2011.6005241Google ScholarGoogle ScholarCross RefCross Ref
  9. Roger C. Mayer, James H. Davis, and F. David Schoorman. 1995. An Integrative Model of Organizational Trust. The Academy of Management Review 20, 3 (1995), 709–734. https://doi.org/10.2307/258792 Publisher: Academy of Management.Google ScholarGoogle ScholarCross RefCross Ref
  10. Bonnie M. Muir and Neville Moray. 1996. Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. Ergonomics 39, 3 (March 1996), 429–460. https://doi.org/10.1080/00140139608964474Google ScholarGoogle ScholarCross RefCross Ref
  11. Stefanos Nikolaidis and Julie Shah. 2013. Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy. In 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, Tokyo, Japan, 33–40. https://doi.org/10.1109/HRI.2013.6483499Google ScholarGoogle ScholarCross RefCross Ref
  12. Juraj Oravec, Daniela Pakšiová, Monika Bakošová, and Miroslav Fikar. 2017. Soft-Constrained Alternative Robust MPC: Experimental Study. IFAC-PapersOnLine 50, 1 (July 2017), 11379–11384. https://doi.org/10.1016/j.ifacol.2017.08.2043Google ScholarGoogle ScholarCross RefCross Ref
  13. Stergios Papanastasiou, Niki Kousi, Panagiotis Karagiannis, Christos Gkournelos, Apostolis Papavasileiou, Konstantinos Dimoulas, Konstantinos Baris, Spyridon Koukas, George Michalos, and Sotiris Makris. 2019. Towards seamless human robot collaboration: integrating multimodal interaction. The International Journal of Advanced Manufacturing Technology 105, 9 (Dec. 2019), 3881–3897. https://doi.org/10.1007/s00170-019-03790-3Google ScholarGoogle ScholarCross RefCross Ref
  14. John K Rempel, John G Holmes, and Mark P Zanna. 1985. Trust in Close Relationships. (1985), 18. https://psycnet.apa.org/record/1985-30794-001Google ScholarGoogle Scholar
  15. Denise M. Rousseau, Sim B. Sitkin, Ronald S. Burt, and Colin Camerer. 1998. Not So Different After All: A Cross-Discipline View Of Trust. Academy of Management Review 23, 3 (July 1998), 393–404. https://doi.org/10.5465/amr.1998.926617 Publisher: Academy of Management.Google ScholarGoogle ScholarCross RefCross Ref
  16. T. Sanders, K. E. Oleson, D. R. Billings, J. Y. C. Chen, and P. A. Hancock. 2011. A Model of Human-Robot Trust: Theoretical Model Development. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 55, 1 (Sept. 2011), 1432–1436. https://doi.org/10.1177/1071181311551298Google ScholarGoogle ScholarCross RefCross Ref
  17. Kimberly Stowers, James Oglesby, Shirley Sonesh, Kevin Leyva, Chelsea Iwig, and Eduardo Salas. 2017. A Framework to Guide the Assessment of Human–Machine Systems. Human Factors (2017), 17. https://journals.sagepub.com/doi/10.1177/0018720817695077Google ScholarGoogle Scholar
  18. Federico Vicentini, Mehrnoosh Askarpour, Matteo G. Rossi, and Dino Mandrioli. 2020. Safety Assessment of Collaborative Robotics Through Automated Formal Verification. IEEE Transactions on Robotics 36, 1 (Feb. 2020), 42–61. https://doi.org/10.1109/TRO.2019.2937471 Conference Name: IEEE Transactions on Robotics.Google ScholarGoogle ScholarCross RefCross Ref

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        • Published in

          cover image ACM Conferences
          HAI '21: Proceedings of the 9th International Conference on Human-Agent Interaction
          November 2021
          447 pages
          ISBN:9781450386203
          DOI:10.1145/3472307

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          Publication History

          • Published: 9 November 2021

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