Participants of one-to-many videoconferencing calls often experience a significant loss of affective feedback due to the limitations of the medium. To address this problem, we present Co-Here: a videoconferencing module that renders participant-driven animations that convey group affect without relying on categorical emotion detection or signaling. Co-Here consolidates and visualizes facial expressions and head movements of fellow videoconferencers, providing a sufficient medium for others to meaningfully construct emotional impressions. Our qualitative user study showed that the system helped users feel a sense of alignment with the emotions of others using the system. Co-Here had the most utility to presentation viewers, who reported that the system offered a low-attention alternative to gauging the sentiment of the crowd.

Our perception of emotion is highly contextual. Changes in the environment can affect our narrative framing, and thus augment our emotional perception of interlocutors. User environments are typically heavily suppressed due to the technical limitations of commercial videoconferencing platforms. As a result, there is often a lack of contextual awareness while participating in a video call, and this affects how we perceive the emotions of conversants. We present a videoconferencing module that visualizes the user's aural environment to enhance awareness between interlocutors. The system visualizes environmental sound based on its semantic and acoustic properties. We found that our visualization system was about 50% effective at eliciting emotional perceptions in users that was similar to the response elicited by environmental sound it replaced.The contributed system provides a unique approach to facilitate ambient awareness on an implicit emotional level in situations where multimodal environmental context is suppressed.

Emotion researchers have begun to converge on the theory that emotions are psychologically and socially constructed. A common assumption in affective robotics is that emotions are categorical brain-body states that can be confidently modeled. But if emotions are constructed, then they are interpretive, ambiguous, and specific to an individual’s unique experience. Constructivist views of emotion pose several challenges to affective robotics: first, it calls into question the validity of attempting to obtain objective measures of emotion through rating scales or biometrics. Second, ambiguous subjective data poses a challenge to computational systems that need structured and definite data to operate. How can a constructivist view of emotion be rectified with these challenges? In this article, we look to psychotherapy for ontological, epistemic, and methodological guidance. These fields (1) already understand emotions to be intrinsically embodied, relative, and metaphorical and (2) have built up substantial knowledge informed by everyday practice. It is our hope that by using interpretive methods inspired by therapeutic approaches, HRI researchers will be able to focus on the practicalities of designing effective embodied emotional interactions.

Social robots must be believable to be effective; but creating believable, affectively expressive robot behaviours requires time and skill. Inspired by the directness with which performers use their voices to craft characters, we introduce Voodle (vocal doodling), which uses the form of utterances -- e.g., tone and rhythm -- to puppet and eventually control robot motion. Voodle offers an improvisational platform capable of conveying hard-to-express ideas like emotion. We created a working Voodle system by collecting a set of vocal features and associated robot motions, then incorporating them into a prototype for sketching robot behaviour. We explored and refined Voodle's expressive capacity by engaging expert performers in an iterative design process. We found that users develop a personal language with Voodle; that a vocalization's meaning changed with narrative context; and that voodling imparts a sense of life to the robot, inviting designers to suspend disbelief and engage in a playful, conversational style of design.

Social robots that physically display emotion invite natural communication with their human interlocutors, enabling applications like robot-assisted therapy where a complex robot's breathing influences human emotional and physiological state. Using DIY fabrication and assembly, we explore how simple 1-DOF robots can express affect with economy and user customizability, leveraging open-source designs.