Synthesized Trust Learning from Limited Human Feedback for Human-Load-Reduced Multi-Robot Deployments
release_wadcn7smqrbapcxu62oimqbviq
by
Yijiang Pang, Chao Huang, Rui Liu
2021
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.
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