Hierarchical Task Learning from Demonstration


PR2-TireRotation

We have developed learning and interaction algorithms to support a human teaching hierarchical task models to a robot using a single demonstration in the context of a mixed-initiative interaction with bidirectional communication. In particular, we have identified and implemented two important heuristics for suggesting task groupings based on the physical structure of the manipulated artifact and on the data flow between tasks. We have evaluated our algorithms with users in a simulated environment and shown both that the overall approach is usable and that the grouping suggestions significantly improve the learning and interaction.

  • Anahita Mohseni-Kabir, Sonia Chernova, Charles Rich, Candy Sidner, and Daniel Miller. Interactive hierarchical task learning from a single demonstration. In ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 1–8. ACM/IEEE, 2015.