The International Journal of Robotics Research · 2011 · 300 citations · 42 references
EngineeringComputer ScienceRobotics EngineeringRobot ControlRobot LearningRoboticsLearning From DemonstrationManipulation LearningPolicy LearningReinforcement LearningAbstractionLearning From Human DemonstrationLearning From Robot DemonstrationManipulator Learning From DemonstrationMobile Manipulator SystemsRobot Learning From DemonstrationRobot Skill AcquisitionRobotic Skill Learning
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.
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