Task-level imitation learning using variance-based movement optimization

— Recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. This makes it increasingly difficult and inconvenient to program these tasks manually. Furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. Therefore, it must be possible to extend the robot’s abilities in an easy and natural way. To address these requirements, this work investigates the topic of imitation learning of motor skills. The focus lies on providing a humanoid robot with the ability to learn new bimanual tasks through the observation of object trajectories. For this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models. The learned information is used to initialize an attractor-based movement generation algorithm that optimizes the re...

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Manuel Mühlig, Michael Gienger, Sven Hellbach, Jochen J. Steil, Christian Goerick
• Keywords: Humanoid Robot, Humanoid Robot Asimo, ICRA 2009, Imitation Learning, Robotics