Robots are entering human environments in increasing numbers. These robots perform functions in shared spaces (e.g. autonomous driving, vacuuming), coordinate with humans (e.g., collaborative manufacturing, object delivery) and leverage sociability (e.g., assistive robots, entertainment). Charismatic robots can add value to human-robot interfaces by abstracting human social rules and interaction strategies into the perception and/or behavior systems of robots. Such systems can be both functional and expressive, but should add social value to people’s experience with these machines. Unfortunately, there are few generalized models of artificial social expression, especially for the most common robot form-factors. Such models do exist in human acting training, and they qualitatively describe how to construct artificial characters and expression. My research seeks to operationalize models from dramaturgical disciplines (e.g., movement training) that aid in the construction of robot nonverbal behaviors. The goals are: 1) to make the model algorithmic, 2) apply the model to low-degree of freedom robots, and 3) refine our parameterizations using the expertise of actors and performers. This talk outlines how we have operationalized the Laban Effort System, a qualitative set of expressive motion descriptives from dance and theater training, to a set of quantitative motion features that robots can use to convey their internal states to people. I will share examples of these feature implementations on three low-degree of freedom robots, including two robot heads and a mobile robot. During naturalistic deployment using the autonomous omni-directional CoBot robots, we find that robot motion characteristics help bystanders interpret the robot’s current state (e.g., rushed), impacting their likelihood of interrupting a robot's task. Expanding on this finding, we are currently investigating additional strategies to flexibly train robot state communications with people in order to effectively include robot expressions into everyday robot tasks.
Heather Knight is a PhD candidate at Carnegie Mellon and founder of Marilyn Monrobot, which features comedy performances by Data the Robot and the annual Robot Film Festival. Her research interests include human-robot interaction, non-verbal machine communications and non-anthropomorphic social robots. She was named to the 2011 Forbes List for 30 under 30 in Science and is featured for her Robot Comedy performances on TED. Her work also includes: robotics and instrumentation at NASA's Jet Propulsion Laboratory, interactive installations with Syyn Labs (including the award winning "This too shall pass" Rube Goldberg Machine music video with OK GO), field applications and sensor design at Aldebaran Robotics, and she is an alumnus from the Personal Robots Group at the MIT Media Lab.