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Abstract-Robot motor control learning is currently one of the most active research areas in robotics. Many learning techniques have been developed for relatively simple problems. However, very few of them have direct applicability in complex robotic systems without assuming prior knowledge about the task, mainly due to three facts. Firstly, they scale badly to continues and high dimensional problems. Secondly, they need too many real robot-environment interactions. Finally, they are not capable of adapting to environment or robot dynamic changes. In order to overcome these problems, we have developed a new algorithm capable of finding from scratch open-loop state-action trajectory solutions by mixing sample-based tree kinodynamic planning with dynamic model learning. Some results demonstrating the viability of this new type of approach in the cart-pole swing-up task problem are presented.

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Robot motor skill learning is currently one of the most active research areas in robotics. Many learning techniques have been developed for relatively simple problems. However, very few of them have direct applicability in complex robotics systems without assuming prior knowledge about the task due to two facts. On one hand, they scale badly to continues and high dimensional problems. On the other hand, they require too many real learning episodes. In this sense, this paper provides a detailed description of an original approach capable of learning from scratch suboptimal solutions and of providing closed-loop motor control policies in the proximity of such solutions. The developed architecture manages the solution in two consecutive phases. The first phase provides an initial openloop solution state-action trajectory by mixing kinodynamic planning with model learning. In the second phase, the initial state trajectory solution is first smoothed and then, a closedloop controller with active learning capabilities is learned in its proximity. We will demonstrate the efficiency of this two phases approach in the Cart-Pole Swing-Up Task problem.