Learning cooperative assembly with the graph representation of a state-action space
Markus Ferch, M. Hochsmann, Jianwei Zhang · 2003
In this paper, we present a method for two robot manipulators to learn cooperative assembly tasks. A learning algorithm based on trial end error is used to find a sequence for each robot to assemble the goal aggregate. 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 novel state-action graph is used to store the reinforcement values of the learning process. The approach is designed in a way that not only exact matches but also similar aggregates are accepted by the system.