Learning cooperative grasping with the graph representation of a state-action space
In this paper we present a method for two robot manipulators to learn cooperative tasks. If a single robot is unable to grasp an object in a certain orientation, it can only continue with the help of other robots. The grasping can be realized by a sequence of cooperative operations that re-orient the object. Several sequences are needed to handle the different situations in which an object is not graspable for the robot. It is shown that a distributed learning method based on a Markov decision process is able to learn the sequences for the involved robots, a master robot that needs to grasp and a helping robot that supports him with the re-orientation. A novel state-action graph is used to store the reinforcement values of the learning process. Further an example of aggregate assembly shows the generality of this approach.
aggregate assembly sequence learning state-action representation multi-robot cooperation