Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained Timescales

TL;DR

This paper introduces a novel framework that enhances human trust estimation in human-robot collaboration by using continuous rewards and fine-grained updates, leading to more accurate and efficient trust modeling.

Contribution

It presents a new method for estimating human trust with beta reputation at fine timescales using continuous rewards, removing the need for manual reward function design.

Findings

Improved accuracy of trust estimation at granular timescales.

Elimination of manual reward function crafting.

Enhanced adaptability of robots in collaborative tasks.

Abstract

When interacting with each other, humans adjust their behavior based on perceived trust. To achieve similar adaptability, robots must accurately estimate human trust at sufficiently granular timescales while collaborating with humans. Beta reputation is a popular way to formalize a mathematical estimation of human trust. However, it relies on binary performance, which updates trust estimations only after each task concludes. Additionally, manually crafting a reward function is the usual method of building a performance indicator, which is labor-intensive and time-consuming. These limitations prevent efficient capture of continuous trust changes at more granular timescales throughout the collaboration task. Therefore, this paper presents a new framework for the estimation of human trust using beta reputation at fine-grained timescales. To achieve granularity in beta reputation, we utilize continuous reward values to update trust estimates at each timestep of a task. We construct a continuous reward function using maximum entropy optimization to eliminate the need for the laborious specification of a performance indicator. The proposed framework improves trust estimations by increasing accuracy, eliminating the need to manually craft a reward function, and advancing toward the development of more intelligent robots.