Automatic weight learning for multiple data sources when learning from demonstration

— Traditional approaches to programming robots are generally inaccessible to non-robotics-experts. A promising exception is the Learning from Demonstration paradigm. Here a policy mapping world observations to action selection is learned, by generalizing from task demonstrations by a teacher. Most Learning from Demonstration work to date considers data from a single teacher. In this paper, we consider the incorporation of demonstrations from multiple teachers. In particular, we contribute an algorithm that handles multiple data sources, and additionally reasons about reliability differences between them. For example, multiple teachers could be inequally proficient at performing the demonstrated task. We introduce Demonstration Weight Learning (DWL) as a Learning from Demonstration algorithm that explicitly represents multiple data sources and learns to select between them, based on their observed reliability and according to an adaptive expert learning inspired approach. We present ...

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Brenna Argall, Brett Browning, Manuela M. Veloso
• Keywords: Data Source, ICRA 2009, Multiple Data Sources, Multiple Teachers, Robotics