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.
Konidaris et al. proposed constructing skill trees REF in the trajectory level to acquire skills from human demonstration using hierarchical RL with options.
Robot learning from demonstration by constructing skill trees
The International Journal of Robotics Research
Computer Science