@article {Konidaris12,
	title = {Robot learning from demonstration by constructing skill trees},
	journal = {The International Journal of Robotics Research},
	volume = {31},
	number = {3},
	year = {2012},
	note = {[[{"fid":3193286,"view_mode":"default","type":"media","attributes":{"height":"360","width":"480","alt":"Autonomous Robot Skill Acquisition - AAAI 2011 Best Student Video on YouTube","title":"Autonomous Robot Skill Acquisition - AAAI 2011 Best Student Video","class":"media-element file-default"}}]]},
	pages = {360{\textendash}375},
	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 eciently 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.},
	author = {George Konidaris and Scott Kuindersma and Roderic Grupen and Andrew Barto}
}
