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Related Experiment Video

Updated: Aug 29, 2025

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

Heterogeneous human-robot task allocation based on artificial trust.

Arsha Ali¹, Hebert Azevedo-Sa^2,3, Dawn M Tilbury^2,4

¹Robotics Department, University of Michigan, Ann Arbor, MI, USA. arshaali@umich.edu.

Scientific Reports

|September 12, 2022

Summary

This summary is machine-generated.

This study introduces a new robot-driven task allocation method for human-robot teams. By learning agent capabilities and using artificial trust, it maximizes team performance and rewards.

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Area of Science:

Robotics
Artificial Intelligence
Human-Computer Interaction

Background:

Effective human-robot collaboration hinges on optimal task allocation.
Current methods may not fully leverage unique human and robot capabilities.
Dynamic adaptation to agent skill is crucial for team performance.

Purpose of the Study:

To present a novel task allocation method for heterogeneous human-robot teams.
To develop a robot-driven approach that learns agent capabilities over time.
To maximize joint team performance by allocating tasks based on artificial trust.

Main Methods:

A task allocation method based on artificial trust from the robot.
Learning agent capabilities using stochastic task outcomes and belief distributions.
Allocating tasks to maximize expected total reward, incorporating trust, task reward, and agent cost.

Main Results:

The artificial trust-based task allocation method demonstrated superior team total reward compared to other methods.
Outperformance was observed both when human capabilities were initially unknown and after convergence.
The method effectively allocates existing and novel tasks to optimize joint performance.

Conclusions:

The proposed artificial trust-based task allocation method enhances human-robot team performance.
The robot's ability to learn agent capabilities and assess trust is key to effective collaboration.
This approach enables dynamic and optimized task distribution in human-robot dyads.