Multi-level task learning from human observation

In this project, a set of segmented skill demonstrations is hierarchically clustered to form a hierarchical task network exploited by the supervisory system for task execution and monitoring. The associated motion patterns and relevant task parameters are also learned. Training data are further exploited to build a generative model of the underlying probability distribution that provides a probabilistic representation of a template skill.