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Modeling and Evaluating Trust Dynamics in Multi-Human Multi-Robot Task Allocation

Ike Obi, Ruiqi Wang, Wonse Jo, Byung-Cheol Min

TL;DR

The paper addresses the challenge of incorporating trust into task allocation for multi-human multi-robot teams. It introduces the Expectation Confirmation Trust (ECT) model, grounded in Expectation Confirmation Theory, which updates trust from the gap between expected and observed robot performance and supports multi-faceted trust, cross-agent learning, and selective decay. Through simulations across 2H-2R, 5H-5R, and 10H-10R configurations, ECT outperforms a no-trust baseline and several existing trust models in metrics like task success rate and completion time, albeit at the cost of higher total travel distance and collision rates, revealing a speed-efficiency-trust trade-off. The work highlights the potential of trust-aware task allocation to boost MH-MR performance and outlines future directions for adaptive trust mechanisms that balance efficiency with reliability in dynamic, multi-agent environments.

Abstract

Trust is essential in human-robot collaboration, particularly in multi-human, multi-robot (MH-MR) teams, where it plays a crucial role in maintaining team cohesion in complex operational environments. Despite its importance, trust is rarely incorporated into task allocation and reallocation algorithms for MH-MR collaboration. While prior research in single-human, single-robot interactions has shown that integrating trust significantly enhances both performance outcomes and user experience, its role in MH-MR task allocation remains underexplored. In this paper, we introduce the Expectation Confirmation Trust (ECT) Model, a novel framework for modeling trust dynamics in MH-MR teams. We evaluate the ECT model against five existing trust models and a no-trust baseline to assess its impact on task allocation outcomes across different team configurations (2H-2R, 5H-5R, and 10H-10R). Our results show that the ECT model improves task success rate, reduces mean completion time, and lowers task error rates. These findings highlight the complexities of trust-based task allocation in MH-MR teams. We discuss the implications of incorporating trust into task allocation algorithms and propose future research directions for adaptive trust mechanisms that balance efficiency and performance in dynamic, multi-agent environments.

Modeling and Evaluating Trust Dynamics in Multi-Human Multi-Robot Task Allocation

TL;DR

The paper addresses the challenge of incorporating trust into task allocation for multi-human multi-robot teams. It introduces the Expectation Confirmation Trust (ECT) model, grounded in Expectation Confirmation Theory, which updates trust from the gap between expected and observed robot performance and supports multi-faceted trust, cross-agent learning, and selective decay. Through simulations across 2H-2R, 5H-5R, and 10H-10R configurations, ECT outperforms a no-trust baseline and several existing trust models in metrics like task success rate and completion time, albeit at the cost of higher total travel distance and collision rates, revealing a speed-efficiency-trust trade-off. The work highlights the potential of trust-aware task allocation to boost MH-MR performance and outlines future directions for adaptive trust mechanisms that balance efficiency with reliability in dynamic, multi-agent environments.

Abstract

Trust is essential in human-robot collaboration, particularly in multi-human, multi-robot (MH-MR) teams, where it plays a crucial role in maintaining team cohesion in complex operational environments. Despite its importance, trust is rarely incorporated into task allocation and reallocation algorithms for MH-MR collaboration. While prior research in single-human, single-robot interactions has shown that integrating trust significantly enhances both performance outcomes and user experience, its role in MH-MR task allocation remains underexplored. In this paper, we introduce the Expectation Confirmation Trust (ECT) Model, a novel framework for modeling trust dynamics in MH-MR teams. We evaluate the ECT model against five existing trust models and a no-trust baseline to assess its impact on task allocation outcomes across different team configurations (2H-2R, 5H-5R, and 10H-10R). Our results show that the ECT model improves task success rate, reduces mean completion time, and lowers task error rates. These findings highlight the complexities of trust-based task allocation in MH-MR teams. We discuss the implications of incorporating trust into task allocation algorithms and propose future research directions for adaptive trust mechanisms that balance efficiency and performance in dynamic, multi-agent environments.
Paper Structure (23 sections, 24 equations, 3 figures, 2 tables)

This paper contains 23 sections, 24 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Evolution of Trust Models in Human-Robot Collaboration
  4. Factors Influencing Trust Dynamics
  5. Methodology
  6. Trust Models
  7. No-trust baseline model
  8. Monir Trust Model
  9. Xu and Dudek Trust Model
  10. Guo and Yang Trust Model
  11. Ali et al. Trust Model
  12. Guo et al. Trust Model
  13. Expectation Confirmation Trust (ECT) Model
  14. Multi‐Faceted Trust
  15. Expectations & Task Histories
  16. ...and 8 more sections

Figures (3)

  • Figure 1: A conceptual illustration of the proposed Expectation Comparison Trust (ECT) Model.
  • Figure 2: Illustration of the core process of our ECT Model.
  • Figure 3: Illustration of our virtual simulation environment containing different agents (UGVs, UAVs, and humans) and different points of interests.