Distributed form closure for convex planar objects through reinforcement learning with local information

Many real world applications would involve grasp of large objects in unstructured environments. Agent-based approach to multi-robot grasp of objects would prove useful under the above circumstances. In this paper, the problem of form closure grasp for planar convex objects by multiple robots is tackled. Contrary to the previous approaches, no a priori information about the shape of the object is assumed, and the robots are not allowed to fully communicate among themselves. A distributed multi-agent based approach using Q-learning is proposed. The state space, action set and learning algorithm are formulated. The results are verified through simulations using a developed Q-learning test bed.