Purpose of Review As robots become increasingly prevalent and capable, the complexity of roles and responsibilities assigned to them as well as our expectations for them will increase in kind. For these autonomous systems to operate safely and efficiently in human-populated environments, they will need to cooperate and coordinate with human teammates. Mental models provide a formal mechanism for achieving fluent and effective teamwork during human-robot interaction by enabling awareness between teammates and allowing for coordinated action. Recent Findings Much recent research in human-robot interaction has made use of standardized and formalized mental modeling techniques to great effect, allowing for a wider breadth of scenarios in which a robotic agent can act as an effective and trustworthy teammate. Summary This paper provides a structured overview of mental model theory and methodology as applied to human-robot teaming. Also discussed are evaluation methods and metrics for various aspects of mental modeling during human-robot interaction, as well as recent emerging applications and open challenges in the field. Keywords Human-robot teaming • Mental models • Human-robot interaction • Theory of mind This article belongs to the Topical Collection on Service and Interactive Robotics

Learning from Demonstration (LfD) enables novice users to teach robots new skills. However, many LfD methods do not facilitate skill maintenance and adaptation. Changes in task requirements or in the environment often reveal the lack of resiliency and adaptability in the skill model. To overcome these limitations, we introduce ARC-LfD: an Augmented Reality (AR) interface for constrained Learning from Demonstration that allows users to maintain, update, and adapt learned skills. This is accomplished through insitu visualizations of learned skills and constraint-based editing of existing skills without requiring further demonstration. We describe the existing algorithmic basis for this system as well as our Augmented Reality interface and the novel capabilities it provides. Finally, we provide three case studies that demonstrate how ARC-LfD enables users to adapt to changes in the environment or task which require a skill to be altered after initial teaching has taken place.

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