https://scholar.dgist.ac.kr/handle/20.500.11750/56664 2026-04-19T13:40:33Z https://scholar.dgist.ac.kr/handle/20.500.11750/59958 Title: Serving Innovation: Seamless Service by Advancing Food Runners With Mobile Manipulation Author(s): Yamsani, Sankalp; Gim, Kevin; Smithline, Tyler; Qiu, Richard; Mineyev, Roman; Hirashima, Kenta; Kang, Sungmin; Park, Kyungseo; Kang, Yoon-Koo; An, Seulbi; Ahn, Sunghwan; Kim, Joohyung Abstract: The Mobile Object Manipulation Operator (MOMO) is an innovative and reconfigurable robotic system that transforms traditional serving robots into mobile manipulators. Leveraging the form factor and mobility of serving robots, MOMO integrates up to three pluggable devices, including six-DoF manipulators of varying sizes or a three-DoF sensor head. Its design incorporates two independent shoulder lifts to enhance vertical reach. The adaptability of the system tailors its capabilities to tasks beyond simple object transportation. As opposed to current food delivery robots, MOMO showcases its ability to remove obstructions from the floor and deliver items to recipients without human intervention. https://scholar.dgist.ac.kr/handle/20.500.11750/59281 Title: Using biopotential and bio-impedance for intuitive human-robot interaction Author(s): Park, Kyungseo; Jeong, Hwayeong; Jung, Yoontae; Suh, Ji-Hoon; Je, Minkyu; Kim, Jung Abstract: The rising interest in robotics and virtual reality has driven a growing demand for intuitive interfaces that enable seamless human-robot interaction (HRI). Bio-signal-based solutions, using biopotential and bio-impedance, offer a promising approach for estimating human motion intention thanks to their ability to capture physiological neuromuscular activity in real time. This Review discusses the potential of biopotential and bio-impedance sensing systems for advancing HRI focusing on the role of integrated circuits in enabling practical applications. Biopotential and bio-impedance can be used to monitor human physiological states and motion intention, making them highly suitable for enhancing motion recognition in HRI. However, as stand-alone modalities, they face limitations related to inter-subject variability and susceptibility to noise, highlighting the need for hybrid sensing techniques. The performance of these sensing modalities is closely tied to the development of integrated circuits optimized for low-noise, low-power operation and accurate signal acquisition in a dynamic environment. Understanding the complementary strengths and limitations of biopotential and bio-impedance signals, along with the advances in integrated circuit technologies for their acquisition, highlights the potential of hybrid, multimodal systems to enable robust, intuitive and scalable HRI. 2025-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57458 Title: A Body-Scale Robotic Skin Using Distributed Multimodal Sensing Modules: Design, Evaluation, and Application Author(s): Yang, Min Jin; Chung, Hyunjo; Kim, Yoonjin; Park, Kyungseo; Kim, Jung Abstract: Robotic systems start to coexist around humans but cannot physically interact as humans do due to an absence of tactile sensitivity across their bodies. Various studies have developed a scalable tactile sensor to grant a body-scale robotic skin, yet many faced drawbacks arising from the rapidly increasing number of sensing elements or a limited sensibility to a wide range of touches. This paper proposes a body-scale robotic skin composed of multimodal sensing modules and a multilayered fabric, simultaneously utilising super-resolution and tomographic transducing mechanisms. These mechanisms employ fewer sensing elements across a large area and complement each other in perceiving a wide range of stimuli humans can sense. Their measurements are processed to encode spatiotemporal properties of touch, which are decoded by a trained convolutional neural network to classify the touch modality, while their computational costs are minimised for on-device computation. The robotic skin was demonstrated on a commercial robotic arm and interpreted human touches for tactile communication, suggesting its capability as a body-scale robotic skin for further physical interaction. © IEEE. 2024-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/56941 Title: Electrode Placement Optimization for Electrical Impedance Tomography Using Active Learning Author(s): Lee, Junhyeong; Park, Kyungseo; Park, Kundo; Kim, Yongtae; Kim, Jung; Ryu, Seunghwa Abstract: Electrical impedance tomography (EIT) offers a versatile imaging modality with a multitude of applications, although it encounters accuracy limitations. Herein, a novel systematic framework is presented that integrates a neural network (NN), active learning, and transfer learning to optimize electrode placement, improving image reconstruction performance based on user-defined metrics. Given the many-to-one mapping between electrode configuration and the performance metric, the approach utilizes a NN that predicts performance metrics from electrode placement input. To maintain NN's prediction accuracy for unseen electrode configurations, performance metrics are maximized while iteratively updating the NN via active learning during the optimization process. Transfer learning is employed to expedite optimization of electrode placements for time-consuming iterative reconstruction techniques by fine-tuning a NN initially trained on one-step reconstruction data. The method is validated using two representative reconstruction methods: one-step reconstruction with Newton's one-step error reconstructor prior and the iterative total variation method. This research underscores the potential of the proposed framework in addressing EIT's inherent limitations and augmenting its performance across diverse applications and reconstruction methods. The framework could potentially contribute to the advancement of noninvasive medical imaging, structural health monitoring, strain sensing, robotics, and other fields that depend on EIT. © 2024 The Authors. Advanced Engineering Materials published by Wiley-VCH GmbH. 2024-05-31T15:00:00Z