Leveraging knowledge asymmetries to evaluate synthesized gesture based communication in human-robot interaction

There has been a considerable effort to help robots learn new tasks from humans. While this effort has led to very promising results, it typically assumes the human partner to be an expert in the task. This social dynamic is a knowledge asymmetry in that some action policy or set of goals is being communicated through an environmental demonstration or using linguistic structure to impart the plan of action to the robot. However, more recent work in human-robot interaction has also been investigating the opposite dynamic: the robot understands a policy or teaching curriculum enough to impart this information back to a child or adult who is unfamiliar with the given task. Current psychological models of communication typically focus on understanding how this dynamic is relaxed over time to create a more peer-to-peer centered interaction, or in other words, one in which both partners are equally capable. However, there are still significant questions surrounding how this dynamic evolves over time. Our focus in this paper is to better understand how humans and robots communicate through nonverbal cues. Gesture is a particularly important part of nonverbal communication that is highly structured and utilizes symbolic motions that can communicate ideas through imagery or spatial referencing. In this paper, we present the idea of using gesture to communicate plans and discuss our early work in translating a navigational path plans to a gesture sequence. We argue that these path plans could one day be interpretable enough for humans to understand and utilize toward achieving their goals.