  We introduce a framework for cooperative manipulation, applied on an
underactuated manipulation problem. Two stationary robotic manipulators are
required to cooperate in order to reposition an object within their shared work
space. Control of multi-agent systems for manipulation tasks cannot rely on
individual control strategies with little to no communication between the
agents that serve the common objective through swarming. Instead a coordination
strategy is required that queries subtasks to the individual agents. We
formulate the problem in a Task And Motion Planning (TAMP) setting, while
considering a decomposition strategy that allows us to treat the task and
motion planning problems separately. We solve the supervisory planning problem
offline using deep Reinforcement Learning techniques resulting into a
supervisory policy capable of coordinating the two manipulators into a
successful execution of the pick-and-place task. Additionally, a benefit of
solving the task planning problem offline is the possibility of real-time
(re)planning, demonstrating robustness in the event of subtask execution
failure or on-the-fly task changes. The framework achieved zero-shot deployment
on the real setup with a success rate that is higher than 90%.
