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Human multi-robot system (MRS) collaboration is demonstrating potentials inwide application scenarios due to the integration of human cognitive skills anda robot team's powerful capability introduced by its multi-member structure.However, due to limited human cognitive capability, a human cannotsimultaneously monitor multiple robots and identify the abnormal ones, largelylimiting the efficiency of the human-MRS collaboration. There is an urgent needto proactively reduce unnecessary human engagements and further reduce humancognitive loads. Human trust in human MRS collaboration reveals humanexpectations on robot performance. Based on trust estimation, the work betweena human and MRS will be reallocated that an MRS will self-monitor and onlyrequest human guidance in critical situations. Inspired by that, a novelSynthesized Trust Learning (STL) method was developed to model human trust inthe collaboration. STL explores two aspects of human trust (trust level andtrust preference), meanwhile accelerates the convergence speed by integratingactive learning to reduce human workload. To validate the effectiveness of themethod, tasks "searching victims in the context of city rescue" were designedin an open-world simulation environment, and a user study with 10 volunteerswas conducted to generate real human trust feedback. The results showed that bymaximally utilizing human feedback, the STL achieved higher accuracy in trustmodeling with a few human feedback, effectively reducing human interventionsneeded for modeling an accurate trust, therefore reducing human cognitive loadin the collaboration.

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