  In this work, we address a task allocation problem for human multi-robot
settings. Given a set of tasks to perform, we formulate a general Mixed-Integer
Linear Programming (MILP) problem aiming at minimizing the overall execution
time while optimizing the quality of the executed tasks as well as human and
robotic workload. Different skills of the agents, both human and robotic, are
taken into account and human operators are enabled to either directly execute
tasks or play supervisory roles; moreover, multiple manipulators can tightly
collaborate if required to carry out a task. Finally, as realistic in human
contexts, human parameters are assumed to vary over time, e.g., due to
increasing human level of fatigue. Therefore, online monitoring is required and
re-allocation is performed if needed. Simulations in a realistic scenario with
two manipulators and a human operator performing an assembly task validate the
effectiveness of the approach.
