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

arXiv: https://arxiv.org/abs/2411.01866 · HTML: https://mlsanity.com/paper/2411.01866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Active Preference-Based Gaussian Process Regression for Reward Learning](https://mlsanity.com/paper/2005.02575/) (2020)
- [Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences](https://mlsanity.com/paper/2006.14091/) (2020)
- [Inverse Reinforcement Learning without Reinforcement Learning](https://mlsanity.com/paper/2303.14623/) (2023)
- [Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning](https://mlsanity.com/paper/1801.04099/) (2018)
- [Modeling and Predicting Trust Dynamics in Human-Robot Teaming: A Bayesian Inference Approach](https://mlsanity.com/paper/2007.13151/) (2020)
- [Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task](https://mlsanity.com/paper/2206.01645/) (2022)
- [Trust-Preserved Human-Robot Shared Autonomy enabled by Bayesian Relational Event Modeling](https://mlsanity.com/paper/2311.02009/) (2023)

## Abstract (arXiv)

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.