Hu, Yuhan, Peide Huang, Mouli Sivapurapu, and Jian Zhang. "ELEGNT: Expressive and Functional Movement Design for Non-anthropomorphic Robot." arXiv preprint arXiv:2501.12493(2025).

https://arxiv.org/abs/2501.12493

Nonverbal behaviors such as posture, gestures, and gaze are essential for conveying internal states, both consciously and unconsciously, in human interaction. For robots to interact more naturally with humans, robot movement design should likewise integrate expressive qualities—such as intention, attention, and emotions—alongside traditional functional considerations like task fulfillment, spatial constraints, and time efficiency. In this paper, we present the design and prototyping of a lamp-like robot that explores the interplay between functional and expressive objectives in movement design. Using a research-through-design methodology, we document the hardware design process, define expressive movement primitives, and outline a set of interaction scenario storyboards. We propose a framework that incorporates both functional and expressive utilities during movement generation, and implement the robot behavior sequences in different function- and social-oriented tasks. Through a user study comparing expression-driven versus function-driven movements across six task scenarios, our findings indicate that expression-driven movements significantly enhance user engagement and perceived robot qualities. This effect is especially pronounced in social-oriented tasks.

K. Brandstätter, B. J. Congdon and A. Steed, "Do you read me? (E)motion Legibility of Virtual Reality Character Representations," 2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Bellevue, WA, USA, 2024, pp. 299-308, doi: 10.1109/ISMAR62088.2024.00044.

We compared the body movements of five virtual reality (VR) avatar representations in a user study (N=53) to ascertain how well these representations could convey body motions associated with different emotions: one head-and-hands representation using only tracking data, one upper-body representation using inverse kinematics (IK), and three full-body representations using IK, motioncapture, and the state-of-the-art deep-learning model AGRoL. Participants’ emotion detection accuracies were similar for the IK and AGRoL representations, highest for the full-body motion-capture representation and lowest for the head-and-hands representation. Our findings suggest that from the perspective of emotion expressivity, connected upper-body parts that provide visual continuity improve clarity, and that current techniques for algorithmically animating the lower-body are ineffective. In particular, the deep-learning technique studied did not produce more expressive results, suggesting the need for training data specifically made for social VR applications.

https://ieeexplore.ieee.org/document/10765392

The Oscar best picture winning movie CODA has helped introduce Deaf culture to many in the hearing community. The capital "D" in Deaf is used when referring to the Deaf culture, whereas small "d" deaf refers to the medical condition. In the Deaf community, sign language is used to communicate, and sign has a rich history in film, the arts, and education. Learning about the Deaf culture in the United States and the importance of American Sign Language in that culture has been key to choosing projects that are useful and usable for the Deaf.