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Collaborative robots that operate in the same immediate environment as human workers have the potential to improve their co-workers' efficiency and quality of work. In this paper we present a taxonomy of assistive behavior types alongside methods that enable a robot to learn assistive behaviors from interactions with a human collaborator during live activity completion. We begin with a brief survey of the state of the art in human-robot collaboration. We proceed to focus on the challenges and issues surrounding the online development of assistive robot behaviors. Finally, we describe approaches for learning when and how to apply these behaviors, as well as for integrating them into a full end-to-end system utilizing techniques derived from the learning from demonstration, policy iteration, and task network communities.

human-robot collaboration; assistive robotics; learning from demonstration; robot manufacturing