Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration
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Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is intensive research on human-robot trust, there is a lack of knowledge about the factors that affect it in synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement characteristics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn is needed to enable a dynamic and spontaneous cooperation. To address this, we conducted a two-phase lab experiment in a mixed-reality environment, in which thirty-two participants collaborated with a virtual CoBot on disassembling traction batteries in a recycling context. In the first phase, we explored the (dynamics of) relevant trust factors during physical human-robot collaboration. In the second phase, we investigated the impact of robot’s reliability and feedback on human trust in robots. Results manifest stronger trust dynamics while dissipating than while accumulating and highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. We detected four factors while trust accumulates (perceived reliability, perceived dependability, perceived predictability, and faith) which do not appear while it dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might shape trust. Further, the robot’s feedback accuracy has a conditional effect on trust depending on the robot’s reliability level. It preserves human trust when a failure is expected but does not affect it when the robot works reliably. This provides a hint to designers on when assurances are necessary and when they are redundant.
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Some authors use correlations between perceived trustworthiness and system capabilities to measure calibration; larger correlation indicates better calibration.
“Some authors work with correlations to get a better sense of perceived trustworthiness and system capabilities [5,12,24,39,66,67,81,94,130,139,142].Here the larger the correlation between perceived trustworthiness and system capabilities, the better the calibration.”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [5]
The Results, under “Reliability Manipulation (TD)” (page unavailable), use Spearman correlations to relate reported trust to factors including perceived reliability, predictability, and dependability. But these are correlations with rated trust factors, not a measure comparing perceived trustworthiness with actual system capabilities; the paper does not say that a larger correlation indicates better calibration. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAlhaji et al. showed system feedback was redundant when the system performed well.
“Yet, Alhaji et al. [5] could show that system feedback was redundant when the system performed well.”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [5]
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Chen, G., Wang, G., Ding, W., Yan, Y., Chen, W., & Shang, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4642985 · Cited by 1 · see citing works
Robot Collaboration and Model Reliance Based on Its Trust in Human-Robot Interaction
Alhaji, B., Prilla, M., & Rausch, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42283-6_2
Self-citationBüttner, S., Söhngen, Y., & Prilla, M. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217660
Self-citationOechsner, C., Deckarm, O., Butz, A. M., & Chiossi, F. (2026) · Augmented Humans International Conference (AHs) · doi:10.1145/3795011.3795012
Vermeulen, J., Caldwell, G. A., Teıxeıra, M. B. F., Burden, A. G., & Guertler, M. R. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812924
Qiao, Y., Wang, H., Li, N., Wang, S., Lei, Y., 武通海, & Cao, J. (2026) · Advanced Robotics Research · doi:10.1002/adrr.202500205
A review of human-robot collaboration in remanufacturing
Grimaldi, N. S., Tian, S., Zheng, M., & Hu, B. (2026) · Journal of remanufacturing · doi:10.1007/s13243-026-00166-1
Trust Resilience in Pedagogical Agents: Will Anthropomorphism Help Against Trust Decline?
Hapsari, F., & Pieschl, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2690098
Guerra, E., Söhngen, Y. N., & Prilla, M. (2026) · Mensch und Computer (MuC) · doi:10.1145/3820253.3820273
Self-citationThe Impact of a Third-Party on Trust of a Human-Robot Dyad
Söhngen, Y. N., Sepp, A., & Prilla, M. (2026) · Mensch und Computer (MuC) · doi:10.1145/3820253.3820278
Self-citationLegendi, M., Syberfeldt, A., & Lamb, M. (2026) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa68377.2026.11725202
- Authors
- Alhaji, B., Prilla, M., & Rausch, A.
- Affiliations
- Clausthal University of Technology (DE)
- Corresponding author
- Alhaji, Basel
- Venue
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Published
- 2021-07-20
- Volume
- 4
- Pages
- 703504-703504
- ISSN
- 2624-8212 (ISSN-L)
- PMC
- PMC8329036
- OpenAlex
- W3185427831
- Cited by
- 29
- References
- 72
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 8 · 2023: 3 · 2021: 1
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Update notices
- corrigendum (2021-10-01)
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Artificial Intelligence (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Publikationsserver der Technischen Universität Clausthal (submittedVersion, cc-by)
- Funders
- Technische Universität Clausthal
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- trust in robots, trust dynamics, trust calibration, robot reliability, robot feedback, perceived predictability, mixed reality, battery disassembly, recycling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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