The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

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 a first implementation of DWL within a simulated robotic domain. Data sources are shown to differ in reliability, and weighting is found impact task execution success. Fur- thermore, DWL is shown to produce appropriate data source weights that improve policy performance.

Keywords

learning skills, robot learning, automatic weight learning

Notes

Sponsor: the Boeing Company, and Carnegie-Mellon University in Qatar and the Qatar Foundation

Associated Lab(s) / Group(s): MultiRobot Lab

Associated Project(s): Treasure Hunt: Pickup Teams

Text Reference

Brenna Argall, Brett Browning, and Manuela Veloso, "Automatic weight learning for multiple data sources when learning from demonstration," the IEEE International Conference on Robotics and Automation (ICRA09), 2009.

BibTeX Reference

@inproceedings{Argall_2009_6993,
   author = "Brenna Argall and Brett Browning and Manuela Veloso",
   title = "Automatic weight learning for multiple data sources when learning from demonstration",
   booktitle = "the IEEE International Conference on Robotics and Automation (ICRA09)",
   year = "2009",
}

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