Exploring Implicit Human Responses to Robot Mistakes in a Learning from Demonstration Task

Robots are becoming more commonplace in human environments, such as schools, homes, hospitals, and work settings, and are expected to accomplish a wide variety of tasks. Given the near infinite number of tasks robots might be expected to perform in these varied settings, it is not feasible for robot designers to completely pre-program machines before they are deployed. Many researchers have suggested this problem can be addressed via end-user robot programming, where users can modify and create new behaviors for their robot to best suit their needs and preferences [2], [1]. Learning from demonstration (LfD) is one such method that enables people to readily develop custom robot behavior [2]. In LfD, a learner automatically creates a mapping between states and actions by watching a teacher perform the task; the learner can then replicate the teacher's actions. The main benefit of LfD is that it is an intuitive way for people to teach robots and does not require the teacher to have highly specialized knowledge, such as the ability to directly program the robot [3]. There has been significant research in how to design and implement LfD systems, including how people want to teach robots. Work by Thomaz et al. [23] showed that LfD systems could be improved for both the teacher and learner if greater communicative channels could be employed during the learning process. We build upon this work, and specifically are interested in ways to enable human teachers to have more efficient and naturalistic interactions, by way of a common human-human interaction (HHI) phenomena: grounding sequences.