Robot learning from demonstration by constructing skill trees


G. Konidaris, S. Kuindersma, R. Grupen, and A. Barto, “Robot learning from demonstration by constructing skill trees,” The International Journal of Robotics Research, vol. 31, no. 3, pp. 360–375, 2012.
cst-ijrr.pdf1.84 MB


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.


Last updated on 05/27/2016