Robot learning from demonstration by constructing skill trees

作者：Konidaris George^*; Kuindersma Scott; Grupen Roderic; Barto Andrew

来源：The International Journal of Robotics Research, 2012, 31(3): 360-375.

DOI：10.1177/0278364911428653

• Learning from demonstration
• motion primitives
• hierarchical reinforcement learning
• motion segmentation
• changepoint detection

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.

• 出版日期2012-3