Learning Distributed Object Pushing: Individual Learning and Distributed Cooperation Protocol

In this paper we study learning in cooperative object pushing systems. The proposed approach is based on the idea of learning individual skills and then mapping the required cooperative behaviors on the learned skills. The control point for each individual robot is chosen so as to simplify the design of the reinforcement signal and reduce the role of delayed reward on individual learning in addition to make cooperative protocol simpler. A fuzzy Q-learning system for learning individual object pushing is proposed, together with a method for coordination among the robots to push the object cooperatively. The coordination method takes into account the dynamics of the object and the learned individual skills. The idea of the coordination protocol is based on the notion of ability and active regions defined in this paper. In effect, the cooperation protocol is mapped to a Q-value based policy. Simulation results supportively show that the robots learn individual and cooperative object pushing efficiently