  Attracted by team scale and function diversity, a heterogeneous multi-robot
system (HMRS), where multiple robots with different functions and numbers are
coordinated to perform tasks, has been widely used for complex and large-scale
scenarios, including disaster search and rescue, site surveillance, and social
security. However, due to the variety of the task requirements, it is
challenging to accurately compose a robot team with appropriate sizes and
functions to dynamically satisfy task needs while limiting the robot resource
cost to a low level. To solve this problem, in this paper, a novel adaptive
cooperation method, inner attention (innerATT), is developed to flexibly team
heterogeneous robots to execute tasks as task types and environment change.
innerATT is designed by integrating a novel attention mechanism into a
multi-agent actor-critic reinforcement learning architecture. With an attention
mechanism, robot capability will be analyzed to flexibly form teams to meet
task requirements. Scenarios with different task variety ("Single Task",
"Double Task", and "Mixed Task") were designed. The effectiveness of the
innerATT was validated by its accuracy in flexible cooperation.
