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Adaptive Grasping of Moving Objects in Dense Clutter via Global-to-Local Detection and Static-to-Dynamic Planning

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arXiv 2025年

作者： Chen, Hao Kiyokawa, Takuya Wan, Weiwei Harada, Kensuke The Department of Systems Innovation Graduate School of Engineering Science Osaka University Toyonaka Osaka560-8531 Japan

Robotic grasping is facing a variety of real-world uncertainties caused by non-static object states, unknown object properties, and cluttered object arrangements. The difficulty of grasping increases with the presence of more uncertainties, where commonly used learning-based approaches struggle to perform consistently across varying conditions. In this study, we integrate the idea of similarity matching to tackle the challenge of grasping novel objects that are simultaneously in motion and densely cluttered using a single RGBD camera, where multiple uncertainties coexist. We achieve this by shifting visual detection from global to local states and operating grasp planning from static to dynamic scenes. Notably, we introduce optimization methods to enhance planning efficiency for this time-sensitive task. Our proposed system can adapt to various object types, arrangements and movement speeds without the need for extensive training, as demonstrated by real-world experiments. Copyright © 2025, The Authors. All rights reserved.

关键词： Clutter (information theory)

Moving-Window Integrated Physics-Data-Based Modeling of Four-Wheel Vehicle Lateral Dynamics

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Automotive Innovation 2025年 1-11页

作者： Wei, Wenpeng Wang, Jinxiang Pi, Dawei Zhuang, Weichao He, Tianyi Yin, Guodong School of Mechanical Engineering Southeast University JiangSu Nanjing 211189 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University JiangSu Nanjing 211189 China Safety and Intelligence of Vehicle Equipment Research Center Nanjing University of Science and Technology JiangSu Nanjing 210094 China Department of Mechanical and Aerospace Engineering Utah State University Logan 84322 UT United States

This paper presents an Integrated physics-data-based (IPDB) modeling of lateral vehicle dynamics with moving-window data snapshots. The IPDB model encodes the fundamental physical principle of four-wheel vehicle motions and simultaneously carries out the adaptiveness of the data-driven approach. Specifically, the traditional physics-based lateral dynamics considering four-wheel interaction are first derived into an affine linear-parameter-varying model, in which the vehicle-related parameters and motion variables are separated. Then, by using the Kronecker product, the IPDB model, directly formulated by the data snapshots in the moving-window fashion, is obtained for system representation. As a result, the IPDB technique rendered model is physically interpretable. The impacts of moving window length on modeling performance are numerically studied. The IPDB model accuracy is validated with data from CarSim simulations and experiments with passenger vehicles under various scenarios. It is further demonstrated that the proposed IPDB model is more data-efficient than other data-driven methods since it only uses the data snapshot in the moving window to update the model recursively. This characteristic enables the IPDB method with online modeling capability to adapt to varying driving scenarios. © The Author(s) 2025.

关键词： Autonomous vehicles Four-wheel vehicle dynamics Integrated physics-data-based modeling Linear parameter varying system

Parkinson's disease tremor prediction towards real-time suppression: A self-attention deep temporal convolutional network approach

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Computers in Biology and Medicine 2025年 188卷 109814-109814页

作者： Tan, Guan Yuan Bakibillah, A.S.M. Chan, Ping Yi Tan, Chee Pin Nurzaman, Surya School of Engineering Monash University Bandar Sunway47500 Malaysia School of Information Technology Monash University Bandar Sunway47500 Malaysia Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo Tokyo152-8552 Japan

Accurate prediction of Parkinson's disease tremor (PDT) is crucial for developing assistive technologies;however, this is challenging due to the nonlinear, stochastic, and nonstationary characteristics of PDT, which substantially vary among patients and their activities. Moreover, most models only have one-step prediction capabilities, which causes delays in real-time applications. This paper proposes a self-attention deep temporal convolutional network (SADTCN) model for the real-time prediction of hand-arm PDT signals from different activities and joint angular motions. The SADTCN can capture both short- and long-term dependencies and complex temporal and spatial dynamics of PDT signals and hence, can effectively adapt to varying tremor characteristics. The performance of the proposed model is evaluated using experimental hand-arm PDT data. The results show that the SADTCN outperforms existing deep learning (DL) models by accurately predicting varying tremor amplitudes and frequencies multi-step ahead. Moreover, we performed spectrum analysis on the measured and predicted signal using the short-time Fourier transform (STFT) as a measure of potential active tremor control and found that SADTCN can accurately determine the transience of tremor amplitude in frequency and time. Finally, we run the Wilcoxon signed-rank statistical test and the results show a statistically significant improvement in the proposed model over the other DL models in all conditions. Therefore, the SADTCN can overcome the nonstationary, nonlinear, and stochastic nature of PDT to perform multi-step prediction with high accuracy, robustness, and generalizability in unseen testing data. © 2025 The Authors

关键词： Prediction models

Estimation of Body Sway Dynamics in a Soft Actuators-based Exercise Game with Squat Motions

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Estimation of Body Sway Dynamics in a Soft Actuators-based E...

IEEE/SICE International Symposium on System Integration

作者： Priyanka Ramasamy Koji Shimatani Yuichi Kurita Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Department of Physical Therapy Prefectural University of Hiroshima

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Postural stability is a significant aspect that needs to be investigated to prevent the risk of injuries and falls in all age groups. Moreover, developing reliable techniques and feedback modalities to enhance the posture control evaluation is crucial. The study investigates the effects of incorporating self-simulated gaming exercises with haptic feedback on posture control in young adults. Participants were randomly assigned to three groups: a control group (n=10) which performed conventional squatting within prescribed time intervals; an intervention group A (n=9), which engaged in virtual reality (VR)-based squatting; and an intervention group B (n=9), which performed squatting in a VR environment while wearing a soft actuator suit. The gaming interface dynamically optimizes the difficulty of squatting based on the user’s knee tremor. We evaluated several center of pressure (COP) parameters: displacement, root mean square (RMS), area, median frequency, and mean velocity in the mediolateral (ML) and anterior-posterior (AP) directions. Statistical analysis using ANOVA revealed significant effects in the ML displacement between the control group and intervention group B (p = 0.027), ML RMS between the control group and intervention group A (p = 0.001), and between the control group and intervention group B (p = 0.001). Additionally, significant differences were observed in the circumference area between the control group and intervention group A (p = 0.035) and between the control group and intervention group B (p = 0.001). The findings indicate that the developed prototype, which provides visual and haptic guidance, shows promise as a tool for improving postural control.

关键词： Visualization Prototypes Games Virtual reality System integration Soft robotics Stability analysis Haptic interfaces Reliability Root mean square

Self-Consistent Field Theory for Semiflexible Gaussian Chain Model

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arXiv 2025年

作者： Oya, Yutaka Kawakatsu, Toshihiro Department of Material Science and Technology Graduate School of Advanced Engineering Tokyo University of Science Tokyo125-8585 Japan Department of Physics Graduate School of Science Tohoku University Sendai980-8578 Japan

Self-consistent field theory (SCFT) is one of the useful methods to simulate phase separated structures of multi-component polymer systems. In this article, we propose an SCFT for semiflexible polymer melts, where the basic equations for the SCFT are derived by introducing a bending stiffness into a flexible Gaussian bead-spring model and taking its continuous limit. Our SCFT is described by a coupled modified diffusion equations for the statistical weight of the chain conformation (path integral), which is a perturbation to semiflexible chains from the flexible Gaussian chain model. Using our modified diffusion equations, we investigated the influences of the bending stiffness on the conformations of symmetric semiflexible diblock copolymer in a strongly segregated lamellar structures and on the order-disorder transition. © 2025, CC BY.

关键词： Conformations

Identifying Inflammatory Bowel Disease-Associated Gene Ontology Groups Using Biological Knowledge-Based Machine Learning

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Identifying Inflammatory Bowel Disease-Associated Gene Ontol...

18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Ersoz, Nur Sebnem Bakir-Gungor, Burcu Yousef, Malik Department of Bioengineering Graduate School of Engineering and Science Abdullah Gul University Kayseri Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Bioengineering Faculty of Life and Natural Sciences Abdullah Gul University Kayseri Turkey Department of Information Systems Zefat Academic College Zefat Israel Zefat Academic College Zefat Israel

Inflammatory bowel disease (IBD) is a chronic inflammatory disease. Complex pathogenesis behind disease formation and progression necessitated the development of new approaches to identify disease related genes and affected gene ontology (GO) terms. In this study, via exploiting GeNetOntology method, we have reanalysed a gene expression data including Crohn’s Disease (CD) and Ulcerative colitis (UC) patients and controls. In order to identify IBD related genes and affected GO terms, GeNetOntology uses GO hierarchy as the biological domain knowledge while performing gene expression data analysis based on machine learning (ML). In the training part of GeNetOntology, genes annotated with selected ontology terms have been utilized to perform a two-class classification task which generates an important set of ontologies as an output. IBD data samples were obtained from peripheral blood and colon tissue. In order to investigate the effect of different collection sites, IBD data have been analysed under different scenarios;i.e., all samples, only tissue samples and only blood samples. Experimental findings indicate that GeNetOntology can successfully determine significant disease-related ontology terms. Performance of the model slightly differs according to the sample source. Via analysing the differences/commonalities between affected gene ontologies under different scenarios, we attempt to enlighten IBD development mechanisms. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

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Tendon-Like Tissue Regeneration Via an Electrospun Nanofiber Scaffold and Cyclic Stretch Stimulation

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SSRN

SSRN 2025年

作者： Morita, Yasuyuki Omata, Seiji Matsubara, Fuga Kimura, Yasuhiro Toku, Yuhki Ju, Yang Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Graduate Department of Micro-Nano Mechanical Science and Engineering Graduate School of Engineering Nagoya University Nagoya Japan Department of Mechanical Engineering Faculty of Engineering Kyushu University Fukuoka Japan State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Kumamoto University Kumamoto Japan

Tendons are important biological tissues that support skeletal muscule activity and lack the ability to heal because of the low number of cells in the ***, regenerative medical techniques have been used to treat tendon tissues in recent *** hypothesized that the unidirectional orientation of the extracellular matrix in tendon tissues, such as collagen, is an important factor in terms of physical and cellular *** addition, because the tendon tissue repeatedly expands during exercise, it is necessary to add a similar mechanical stimulus to the regenerated tendon *** such an environment, it is necessary to appropriately induce undifferentiated mesenchymal stem cells to differentiate into tendon cells to generate the physical structure of the tendon ***, we aimed to clarify the relationship between the induction of tendon cell differentiation and changes in tissue structure in response to mechanical stimuli during tendon tissue *** study used electrospinning to stack nanofibers on silicone rubber substrates, which are incubator materials for the effecient and reproducible fabrication of nanofibers. The layered nanofiber film was capable of building up sufficient thickness and succeeded in building up a thickness of 100 $\mu$*** effects of the elasticity and hardness of the incubator material seemed to be sufficiently small because the laminate was several times thicker than the cells, which had an adherent and spreading *** fiber diameter of the stacked nanofibers is 0.2-1 $\mu$m, which is sufficiently smaller than the size of cells, so the cell adhesion group formed when cells adhere is then small and can easily be strongly induced in the fiber *** results showed that when scleraxis-programmed tendon progenitors in human mesenchymal stem cell (hMSC-SCX) were seeded in a culture vessel with a nanofiber layer, cell proliferation was promoted and there was no decrease in gene expression spe

关键词： Stem cells

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

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arXiv 2025年

作者： Roh, Daeyoung Han, Donghee Kim, Daehee Han, Keejun Yi, Mun Graduate School of Data Science KAIST Korea Republic of School of Computer Engineering Hansung University Korea Republic of Department of Industrial and Systems Engineering KAIST Korea Republic of

Hypergraphs provide a superior modeling framework for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraph-based contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the high-order correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce high-quality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://***/Noverse0/***. © 2025, CC BY.

关键词： Contrastive Learning

A Cholesky decomposition-based asset selection heuristic for sparse tangent portfolio optimization

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arXiv 2025年

作者： Bae, Hyunglip Jeon, Haeun Park, Minsu Lee, Yongjae Kim, Woo Chang Department of Industrial and Systems Engineering KAIST Korea Republic of Graduate School of Data Science KAIST Korea Republic of Department of Industrial Engineering UNIST Korea Republic of Artificial Intelligence Graduate School UNIST Korea Republic of

In practice, including large number of assets in mean-variance portfolios can lead to higher transaction costs and management fees. To address this, one common approach is to select a smaller subset of assets from the larger pool, constructing more efficient portfolios. As a solution, we propose a new asset selection heuristic which generates a pre-defined list of asset candidates using a surrogate formulation and re-optimizes the cardinality-constrained tangent portfolio with these selected assets. This method enables faster optimization and effectively constructs portfolios with fewer assets, as demonstrated by numerical analyses on historical stock returns. Finally, we discuss a quantitative metric that can provide a initial assessment of the performance of the proposed heuristic based on asset covariance. © 2025, CC BY.

关键词： Constrained optimization