  As multi-robot systems (MRS) are widely used in various tasks such as natural
disaster response and social security, people enthusiastically expect an MRS to
be ubiquitous that a general user without heavy training can easily operate.
However, humans have various preferences on balancing between task performance
and safety, imposing different requirements onto MRS control. Failing to comply
with preferences makes people feel difficult in operation and decreases human
willingness of using an MRS. Therefore, to improve social acceptance as well as
performance, there is an urgent need to adjust MRS behaviors according to human
preferences before triggering human corrections, which increases cognitive
load. In this paper, a novel Meta Preference Learning (MPL) method was
developed to enable an MRS to fast adapt to user preferences. MPL based on meta
learning mechanism can quickly assess human preferences from limited
instructions; then, a neural network based preference model adjusts MRS
behaviors for preference adaption. To validate method effectiveness, a task
scenario "An MRS searches victims in an earthquake disaster site" was designed;
20 human users were involved to identify preferences as "aggressive", "medium",
"reserved"; based on user guidance and domain knowledge, about 20,000
preferences were simulated to cover different operations related to "task
quality", "task progress", "robot safety". The effectiveness of MPL in
preference adaption was validated by the reduced duration and frequency of
human interventions.
