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 in-situ 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.

My thesis research incorporates high-level abstract behavioral requirements, called ‘conceptual constraints’, into the modeling processes of robot Learning from Demonstration (LfD) techniques. My most recent work introduces an LfD algorithm called Concept Constrained Learning from Demonstration. This algorithm encodes motion planning constraints as temporal Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Future work will incorporate conceptual constraints into human-aware motion planning algorithms. Additionally, my research will investigate how these concept constrained algorithms and models are best incorporated into effective interfaces for end-users.

Learning from demonstration (LfD) has enabled robots to rapidly gain new skills and capabilities by leveraging examples provided by novice human operators. While effective, this training mechanism presents the potential for sub-optimal demonstrations to negatively impact performance due to unintentional operator error. In this work we introduce Concept Constrained Learning from Demonstration (CC-LfD), a novel algorithm for robust skill learning and skill repair that incorporates annotations of conceptually-grounded constraints (in the form of planning predicates) during live demonstrations into the LfD process. Through our evaluation, we show that CC-LfD can be used to quickly repair skills with as little as a single annotated demonstration without the need to identify and remove low-quality demonstrations. We also provide evidence for potential applications to transfer learning, whereby constraints can be used to adapt demonstrations from a related task to achieve proficiency with few new demonstrations required.