  This paper presents a learning framework to estimate an agent capability and
task requirement model for multi-agent task allocation. With a set of team
configurations and the corresponding task performances as the training data,
linear task constraints can be learned to be embedded in many existing
optimization-based task allocation frameworks. Comprehensive computational
evaluations are conducted to test the scalability and prediction accuracy of
the learning framework with a limited number of team configurations and
performance pairs. A ROS and Gazebo-based simulation environment is developed
to validate the proposed requirements learning and task allocation framework in
practical multi-agent exploration and manipulation tasks. Results show that the
learning process for scenarios with 40 tasks and 6 types of agents uses around
12 seconds, ending up with prediction errors in the range of 0.5-2%.
