Open and closed-loop task space trajectory control of redundant robots using learned models

This paper presents a comparison of open-loop and closed-loop control strategies for tracking a task space trajectory, using redundant robots. We do not assume any knowledge of the analytical forward and inverse kinematics, relying instead on learning these models online, while executing a desired task. Specifically, we employ a recent learning algorithm that allows to learn a probabilistic model from which both the forward and inverse solutions can be obtained, as well as the Jacobian of the kinematics map. Such learned model can then be used to implement both types of control. Moreover, the multi-valued solutions provided by the learned model can be applied to redundant systems in which an infinite number of inverse solutions may exist. We present experiments with a simulated version of the iCub, a highly redundant humanoid robot, in which this learned model is employed to execute both open-loop and closed-loop trajectory control. We show the advantages and drawbacks of both control strategies, and we propose a way to combine them to deal with sensor noise and failures, showing the benefits of using a learning algorithm that can simultaneously provide forward and inverse predictions.