SARSA-based reinforcement learning for motion planning in serial manipulators

In this paper we investigate an application in which a serial manipulator is engaged in a task driven state transition learning through a set of basic behaviours (i.e. inherited actions). The approach is based on an extension of the SARSA reinforcement learning algorithm. In particular, the case under study consists in the control of the end-effector position sequences of a custom serial manipulator (i.e. the MiniARM) in a constrained shortest path problem. In order to test performances of the overall algorithm and the improvement beyond the state of the art, those strategies have been implemented both in simulation and in a real hardware environment. Results have been analyzed in terms of learning time and iterations needed to complete the assigned task.