Movement seems to encode information. How does this work? We know that animals, including humans, use the motion of counterparts to produce coordinated, social behaviors. But how do we resolve the discrete measures of communication and information theory with the continuous laws of motion and mechanics? Answering these questions is critical to developing expressive robotic systems that integrate seamlessly with natural counterparts – a goal that has increasing urgency as robots move out of factories and into workplaces and homes. The RAD Lab uses an information-theoretic model (where artificial systems are modeled as a source communicating across an environmental channel to a human receiver) to understand this process.
C. Cuan, E. Berl, and A. LaViers. “Measuring Human Perceptions of Expressivity in Natural and Artificial Systems Through the Live Performance Piece Time to Compile.” Paladyn. Journal of Behavioral Robotics. (Special Issue on Social Robots in Therapy: Focusing on Autonomy and Ethical Challenges). 10(1), 364–379. 2019.