Collaborative control in a humanoid dynamic task

This paper describes a collaborative control scheme that governs the dynamic behavior of an articulated mobile robot with several degrees of freedom (DOF) and redundancies. These types of robots need a high level of coordination between the motors performance to complete their motions. In the employed scheme, the actuators involved in a specific task share information, computing integrated control actions. The control functions are found using a stochastic reinforcement learning technique allowing the robot to automatically generate them based on experiences. This type of control is based on a modularization principle: complex overall behavior is the result of the interaction of individual simple components. Unlike the standard procedures, this approach is not meant to follow a trajectory generated by a planner, instead, the trajectory emerges as a consequence of the collaboration between joints movements while seeking the achievement of a goal. The learning of the sensorimotor coordination in a simulated humanoid is presented as a demonstration.