Research
My research work seeks to develop interactive social robots for scenarios involving children. In particular, my work applies artificial intelligence and machine learning techniques to better perceive, understand, and respond to expressive human behavior. Four key themes in my graduate work include:
- Exploring human perceptions of social robots
- Evaluating human-robot interaction effects on performance
- Auditing expression perception algorithms
- Modeling human perception of facial expression
Select publications for these themes are highlighted below.
Exploring Human Perceptions of Social Robots
The Effect of Conceptual Embodiment on Human-Robot Trust During a Youth Emotion Classification Task
Bryant, D., Xu, J., Rogers, K., & Howard, A.
IEEE International Conference on Advanced Robotics & Its Social Impacts (ARSO) 2021 *Best Paper Award Recipient*
Why should we gender? The effect of robot gendering and occupational stereotypes on human trust and perceived competency
Bryant, D., Borenstein, J. & Howard, A.
ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2020
Does removing stereotype priming remove bias? A pilot human-robot interaction study
Ogunyale, T., Bryant, D., & Howard, A.
Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT-ML) 2018
Evaluating HRI Effects on Performance
The Effect of Robot vs. Human Corrective Feedback on Children’s Intrinsic Motivation
Bryant, D., Xu, J. & Howard, A.
ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2019
Robot therapist versus human therapist: Evaluating the effect of corrective feedback on human motor performance
Xu, J., Bryant, D. & Howard, A.
International Symposium on Medical Robotics (ISMR) 2018
Would You Trust a Robot Therapist? Validating the Equivalency of Trust in Human-Robot Healthcare Scenarios
Xu, J., Bryant, D. & Howard, A.
IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) 2018
Auditing Perception Algorithms
Age bias in emotion detection: an analysis of facial emotion recognition performance on young, middle-aged, and older adults
Kim, E., Bryant, D., Srikanth, D., & Howard, A.
AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2021
A comparative analysis of emotion-detecting AI systems with respect to algorithm performance and dataset diversity
Bryant, D. & Howard, A.
AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2019
Modeling Human Perception of Facial Expression
Multi-dimensional, nuanced, subjective: measuring the perception of facial expressions
Bryant, D., Deng, S., Sephus, N., Xia, W., & Perona, P.
The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2022
Additional Publications
Bryant, D., Etiene, T., Howard, A., Smart, W.D., and Glas, D. (2023) Teaching a robot where to park: A scalable crowdsourcing approach. In The 30th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN ’23).
Chen, Y., Xu, J., Bryant, D., & Howard, A. (2022). Effects of Human and Robot Feedback on Shaping Human Movement Behaviors during Reaching Tasks. International Journal of Human-Computer Interaction, 1-10.
Rogers, K., Bryant, D., and Howard, A. (2020). Robot Gendering: Influences on Trust, Occupational Competency, and Preference of Robot Over Human. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20 extended abstract).
Bryant, D., Boyd, J., Harris, J., Smith, M., Garcia-Vergara, S., Chen, Y., & Howard, A. (2017). An Infant Smart-Mobile System to Encourage Kicking Movements in Infants At-Risk of Cerebral Palsy. In Proceedings of the 2017 IEEE International Workshop on Advanced Robotics and its Social Impacts (ARSO ‘17).
Bryant, D., Liles, K. R., & Beer, J. M. (2017). Developing a Robot Hip-Hop Dance Game to Engage Rural Minorities in Computer Science. In Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’17 LBR).
Liles, K. R., Bryant, D., & Beer, J. M. (2017). How Can Social Robots Motivate Students to Practice Math? In Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’17 LBR).