Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots

• Main Authors: Wairagkar, Maitreyee, Lima, Maria R, Bazo, Daniel, Craig, Richard, Weissbart, Hugo, Etoundi, Appolinaire C, Reichenbach, Tobias, Iyenger, Prashant, Vaswani, Sneh, James, Christopher, Barnaghi, Payam, Melhuish, Chris, Vaidyanathan, Ravi
• Format: Journal Article
• Language: English
• Published: 08.12.2020
• Subjects: Computer Science - Robotics
• DOI: 10.48550/arxiv.2012.04511

We introduce the conceptual formulation, design, fabrication, control and commercial translation with IoT connection of a hybrid-face social robot and validation of human emotional response to its affective interactions. The hybrid-face robot integrates a 3D printed faceplate and a digital display to simplify conveyance of complex facial movements while providing the impression of three-dimensional depth for natural interaction. We map the space of potential emotions of the robot to specific facial feature parameters and characterise the recognisability of the humanoid hybrid-face robot's archetypal facial expressions. We introduce pupil dilation as an additional degree of freedom for conveyance of emotive states. Human interaction experiments demonstrate the ability to effectively convey emotion from the hybrid-robot face to human observers by mapping their neurophysiological electroencephalography (EEG) response to perceived emotional information and through interviews. Results show main hybrid-face robotic expressions can be discriminated with recognition rates above 80% and invoke human emotive response similar to that of actual human faces as measured by the face-specific N170 event-related potentials in EEG. The hybrid-face robot concept has been modified, implemented, and released in the commercial IoT robotic platform Miko (My Companion), an affective robot with facial and conversational features currently in use for human-robot interaction in children by Emotix Inc. We demonstrate that human EEG responses to Miko emotions are comparative to neurophysiological responses for actual human facial recognition. Finally, interviews show above 90% expression recognition rates in our commercial robot. We conclude that simplified hybrid-face abstraction conveys emotions effectively and enhances human-robot interaction.