Christopher A. Sanchez

Brian J. Zhang, Knut Peterson, Christopher A. Sanchez et al.

All robots create consequential sound -- sound produced as a result of the robot's mechanisms -- yet little work has explored how sound impacts human-robot interaction. Recent work shows that the sound of different robot mechanisms affects perceived competence, trust, human-likeness, and discomfort. However, the physical sound characteristics responsible for these perceptions have not been clearly identified. In this paper, we aim to explore key characteristics of robot sound that might influence perceptions. A pilot study from our past work showed that quieter and higher-pitched robots may be perceived as more competent and less discomforting. To better understand how variance in these attributes affects perception, we performed audio manipulations on two sets of industrial robot arm videos within a series of four new studies presented in this paper. Results confirmed that quieter robots were perceived as less discomforting. In addition, higher-pitched robots were perceived as more energetic, happy, warm, and competent. Despite the robot's industrial purpose and appearance, participants seemed to prefer more "cute" (or "kawaii") sound profiles, which could have implications for the design of more acceptable and fulfilling sound profiles for human-robot interactions with practical collaborative robots.

Anahita Sanandaji, Cindy Grimm, Ruth West et al.

Understanding 2D cross-sections of 3D structures is a crucial skill in many disciplines, from geology to medical imaging. Cross-section inference in the context of 3D structures requires a complex set of spatial/visualization skills including mental rotation, spatial structure understanding, and viewpoint projection. Prior studies show that experts differ from novices in these, and other, skill dimensions. Building on a previously developed model that hierarchically characterizes the specific spatial sub-skills needed for this task, we have developed the first domain-agnostic, computer-based training tool for cross-section understanding of complex 3D structures. We demonstrate, in an evaluation with 60 participants, that this interactive tool is effective for increasing cross-section inference skills for a variety of structures, from simple primitive ones to more complex biological structures.