Towards Manipulator Learning by Demonstration and Reinforcement Learning

Abstract: This thesis address how robotic arms, called manipulators, can learn a task demonstrated by a teacher. The concept of showing a robot a task, instead of manually programming it, is appealing since it makes it easier to instruct robots. This thesis will introduce the basics of manipulators and techniques suitable for robot learning including an introduction to reinforcement learning. Also a number of other researchers' work are reviewed from the viewpoint of how they apply robot learning from a teacher, and how this knowledge can be reused when a similar problem is faced. One key part of this thesis is an overview of the field Robot Learning from Demonstration, focusing on robotic manipulators, but work including humanoids and mobile robots are also covered. Challenges, such as how to learn from the demonstration, and what to learn are presented together with related work. Initial experiments on learning from a teacher's demonstration, have been carried out using a manipulator and a motion capturing device as a platform. The experiments investigated areposition teaching of a robotic arm using neural networks and a minimum distance classifier,reinforcement learning algorithm for a reaching task, where a demonstrated trajectory was used as bias.Based on the presented work we suggest a future work direction and that provide the robot with some basic behaviours needed to learn other higher level tasks.