  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 make robots understand human expectations 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.
