Automatic selection of task spaces for imitation learning

Abstract— Previous work [1] shows that the movement representation in task spaces offers many advantages for learning object-related and goal-directed movement tasks through imitation. It allows to reduce the dimensionality of the data that is learned and simplifies the correspondence problem that results from different kinematic structures of teacher and robot. Further, the task space representation provides a first generalization, for example wrt. differing absolute positions, if bi-manual movements are represented in relation to each other. Although task spaces are widely used, even if they are not mentioned explicitly, they are mostly defined a priori. This work is a step towards an automatic selection of task spaces. Observed movements are mapped into a pool of possibly even conflicting task spaces and we present methods that analyze this task space pool in order to acquire task space descriptors that match the observation best. As statistical measures cannot explain importa...

• Type: Conference
• Year: 2009
• Where: IROS
• Authors: Manuel Mühlig, Michael Gienger, Jochen J. Steil, Christian Goerick
• Keywords: IROS 2009, Robotics, Task Space, Task Space Descriptors, Task Space Representation