Semantic structure for robotic teaching and learning

Instructing human novices on complex tasks in non-standardized environments are an underexplored potential use for social co-robots, since instruction and skill transfer involving human experts can require an enormous commitment of time and resources. In this paper, we enable a humanoid Baxter robot to build a semantically accessible framework for task learning, teaching and representation via active learning with human experts using hierarchical semantic labels. This process not only helps the robot to learn tasks from expert demonstrations, but later improves the ability of the robot to teach novice human operators. Our results show that the better-understood learning from demonstration (LfD) task is greatly enhanced by the active learning and mutual semantic structure building in a expert-robot partnership, while the robot's ability to teach novices is improved, though the results are suggestive rather than conclusive at this point. We discuss the important aspects and power of learning and teaching from demonstration and how both benefit from communication and joint human-robot creation of semantic hierarchies.