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Hyunyoung Kim

Publications and source records attributed to Hyunyoung Kim.

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Dimple-Encoded Reprogrammable Origami

Programmable folding of elastic sheets typically relies on predefined flexible creases or active materials-enabled hinges, which lack intrinsic bistability and limit reprogrammability within a single structure. Here, we present a dimple-encoded origami platform that converts bistable dimple snapping into spatially addressable hinges with prescribed folding angles in a continuous sheet. This interaction-enabled mechanism enables the design of distributed hinge networks through the arrangement and selective inversion of dimples. We establish folding-angle design charts that can be directly used to select local dimple arrangements for target fold angle, forming a practical hinge library without altering the underlying unit geometry. Using this approach, a single dimpled sheet can be reprogrammed to realize multiple distinct configurations, such as triangle, square, and pentagon shapes. We further extend the method to flat-to-3D morphing of polyhedral origami and validate the results through experiments and finite element simulations. As demonstrations, we realize self-supporting cubic shells with enhanced impact resistance and partially deployable cube configurations that remain stable upon opening, highlighting their potential for protective enclosures and deployable architectural structures. The proposed strategy provides a fabrication-friendly route to reprogrammable shape-morphing and adaptive mechanical systems.

cond-mat.soft

Charting Visual Impression of Robot Hands

A wide variety of robotic hands have been designed to date. Yet, we do not know how users perceive these hands and feel about interacting with them. To inform hand design for social robots, we compiled a dataset of 73 robot hands and ran an online study, in which 160 users rated their impressions of the hands using 17 rating scales. Next, we developed 17 regression models that can predict user ratings (e.g., humanlike) from the design features of the hands (e.g., number of fingers). The models have less than a 10-point error in predicting the user ratings on a 0-100 scale. The shape of the fingertips, color scheme, and size of the hands influence the user ratings the most. We present simple guidelines to improve user impression of robot hands and outline remaining questions for future work.

cs.RO