Dynamic movement primitives andreinforcement learning for adapting alearned skill
Abstract: Traditionally robots have been preprogrammed to execute specific tasks. Thisapproach works well in industrial settings where robots have to execute highlyaccurate movements, such as when welding. However, preprogramming a robot isalso expensive, error prone and time consuming due to the fact that every featuresof the task has to be considered. In some cases, where a robot has to executecomplex tasks such as playing the ball-in-a-cup game, preprogramming it mighteven be impossible due to unknown features of the task. With all this in mind,this thesis examines the possibility of combining a modern learning framework,known as Learning from Demonstrations (LfD), to first teach a robot how toplay the ball-in-a-cup game by demonstrating the movement for the robot, andthen have the robot to improve this skill by itself with subsequent ReinforcementLearning (RL). The skill the robot has to learn is demonstrated with kinestheticteaching, modelled as a dynamic movement primitive, and subsequently improvedwith the RL algorithm Policy Learning by Weighted Exploration with the Returns.Experiments performed on the industrial robot KUKA LWR4+ showed that robotsare capable of successfully learning a complex skill such as playing the ball-in-a-cupgame.
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