Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained Timescales
This work addresses the need for more adaptive and intelligent robots in human-robot collaboration, though it is incremental as it builds on existing beta reputation methods.
The paper tackled the problem of robots accurately estimating human trust during collaboration by introducing a framework that uses beta reputation with continuous reward values at fine-grained timescales, resulting in improved accuracy and elimination of manual reward function crafting.
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.