To enable natural and productive human-robot interaction (HRI), a co-robot must both understand and control "proxemics" -- the social use of space -- in order to communicate in ways commonly used and understood by humans. This project focuses on answering the question: How do social (speech and gesture), environmental (loud noises and low lighting), and personal (hearing and visual impairments) factors influence positioning and communication between humans and co-robots, and how should a co-robot adjust its social behaviors to maximize human perception of its social signals?
The project will develop principled computational models for the recognition and control of proxemic co-robot behavior in HRI using both telepresence and autonomous co-robots. The research will establish a foundational component of HRI for co-robotics, with specific impact on special needs users in socially assistive contexts -- particularly the elderly, both aging in place and in institutions -- with the goal of mitigating isolation and depression, and encouraging exercise and socialization.
Broader impacts: The work will inform robot design and control, and provide software and a corpus of public HRI data for use by researchers worldwide. Beyond robotics, the project promises to inform, validate, and extend longstanding research in the social sciences. This project also includes a strong public and K-12 outreach component consisting of weaving the HRI themes being developed into annual regional and international outreach events. The events feature large-scale open houses and educational workshops with interactive demonstrations and hands-on activities that highlight human factors in computational systems as an effective means of increasing interest in STEM-related activities.
