Robot learning from demonstration by constructing skill trees

Authors:
George Konidaris;Scott Kuindersma;Roderic Grupen;Andrew Barto
Affiliations:
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA, Autonomous Learning Laboratory, University of Massachusetts Amherst, Amherst, MA ...;Autonomous Learning Laboratory, University of Massachusetts Amherst, Amherst, MA, USA, Laboratory for Perceptual Robotics, University of Massachusetts Amherst, Amherst, MA, USA;Laboratory for Perceptual Robotics, University of Massachusetts Amherst, Amherst, MA, USA;Autonomous Learning Laboratory, University of Massachusetts Amherst, Amherst, MA, USA
Venue:
International Journal of Robotics Research
Year:
2012

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Abstract

We describe CST, an online algorithm for constructing skill trees from demonstration trajectories. CST segments a demonstration trajectory into a chain of component skills, where each skill has a goal and is assigned a suitable abstraction from an abstraction library. These properties permit skills to be improved efficiently using a policy learning algorithm. Chains from multiple demonstration trajectories are merged into a skill tree. We show that CST can be used to acquire skills from human demonstration in a dynamic continuous domain, and from both expert demonstration and learned control sequences on the uBot-5 mobile manipulator.