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Youngsun Kim

Publications and source records attributed to Youngsun Kim.

2 recordsLinked to original sources

Tuning Multipolar Mie Scattering of Particles on a Dielectric-Covered Mirror

Optically resonant particles are key building blocks of many nanophotonic devices such as optical antennas and metasurfaces. Because the functionalities of such devices are largely determined by the optical properties of individual resonators, extending the attainable responses from a given particle is highly desirable. Practically, this is usually achieved by introducing an asymmetric dielectric environment. However, commonly used simple substrates have limited influences on the optical properties of the particles atop. Here, we show that the multipolar scattering of silicon microspheres can be effectively modified by placing the particles on a dielectric-covered mirror, which tunes the coupling between the Mie resonances of microspheres and the standing waves and waveguide modes in the dielectric spacer. This tunability allows selective excitation, enhancement, and suppression of the multipolar resonances and enables scattering at extended wavelengths, providing new opportunities in controlling light-matter interactions for various applications. We further demonstrate with experiments the detection of molecular fingerprints by single-particle mid-infrared spectroscopy, and, with simulations strong optical repulsive forces that could elevate the particles from a substrate.

physics.optics

Robot-Assisted Feeding: Generalizing Skewering Strategies across Food Items on a Realistic Plate

A robot-assisted feeding system must successfully acquire many different food items. A key challenge is the wide variation in the physical properties of food, demanding diverse acquisition strategies that are also capable of adapting to previously unseen items. Our key insight is that items with similar physical properties will exhibit similar success rates across an action space, allowing the robot to generalize its actions to previously unseen items. To better understand which skewering strategy works best for each food item, we collected a dataset of 2450 robot bite acquisition trials for 16 food items with varying properties. Analyzing the dataset provided insights into how the food items' surrounding environment, fork pitch, and fork roll angles affect bite acquisition success. We then developed a bite acquisition framework that takes the image of a full plate as an input, segments it into food items, and then applies our Skewering-Position-Action network (SPANet) to choose a target food item and a corresponding action so that the bite acquisition success rate is maximized. SPANet also uses the surrounding environment features of food items to predict action success rates. We used this framework to perform multiple experiments on uncluttered and cluttered plates. Results indicate that our integrated system can successfully generalize skewering strategies to many previously unseen food items.

cs.RO