Synthesized Trust Learning from Limited Human Feedback for Human-Load-Reduced Multi-Robot Deployments

TL;DR

This paper introduces a novel Synthesized Trust Learning method that models human trust in multi-robot systems, reducing human workload and cognitive load by enabling robots to self-monitor and request guidance only in critical situations.

Contribution

The paper presents a new trust modeling approach that combines trust level and preference, integrating active learning to improve accuracy with minimal human feedback.

Findings

STL achieves higher trust modeling accuracy with fewer human feedbacks.

The method reduces human interventions and cognitive load in multi-robot collaboration.

Validated through simulation and user study with 10 volunteers.

Abstract

Human multi-robot system (MRS) collaboration is demonstrating potentials in wide application scenarios due to the integration of human cognitive skills and a robot team's powerful capability introduced by its multi-member structure. However, due to limited human cognitive capability, a human cannot simultaneously monitor multiple robots and identify the abnormal ones, largely limiting the efficiency of the human-MRS collaboration. There is an urgent need to proactively reduce unnecessary human engagements and further reduce human cognitive loads. Human trust in human MRS collaboration reveals human expectations on robot performance. Based on trust estimation, the work between a human and MRS will be reallocated that an MRS will self-monitor and only request human guidance in critical situations. Inspired by that, a novel Synthesized Trust Learning (STL) method was developed to model human trust in the collaboration. STL explores two aspects of human trust (trust level and trust preference), meanwhile accelerates the convergence speed by integrating active learning to reduce human workload. To validate the effectiveness of the method, tasks "searching victims in the context of city rescue" were designed in an open-world simulation environment, and a user study with 10 volunteers was conducted to generate real human trust feedback. The results showed that by maximally utilizing human feedback, the STL achieved higher accuracy in trust modeling with a few human feedback, effectively reducing human interventions needed for modeling an accurate trust, therefore reducing human cognitive load in the collaboration.