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A Unified Formulation of Geometry-aware Discrete Dynamic Movement Primitives

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arXiv 2022年

作者： Abu-Dakka, Fares J. Saveriano, Matteo Kyrki, Ville Electronic and Computer Science Department Faculty of Engineering Mondragon Unibertsitatea Arrasate20500 Spain Automatic Control Lab Department of Industrial Engineering University of Trento Trento Italy Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Aalto Finland

Learning from demonstration (LfD) is considered as an efficient way to transfer skills from humans to robots. Traditionally, LfD has been used to transfer Cartesian and joint positions and forces from human demonstrations. The traditional approach works well for some robotic tasks, but for many tasks of interest, it is necessary to learn skills such as orientation, impedance, and/or manipulability that have specific geometric characteristics. An effective encoding of such skills can be only achieved if the underlying geometric structure of the skill manifold is considered and the constrains arising from this structure are fulfilled during both learning and execution. However, typical learned skill models such as dynamic movement primitives (DMPs) are limited to Euclidean data and fail in correctly embedding quantities with geometric constraints. In this paper, we propose a novel and mathematically principled framework that uses concepts from Riemannian geometry to allow DMPs to properly embed geometric constrains. The resulting DMP formulation can deal with data sampled from any Riemannian manifold including, but not limited to, unit quaternions and symmetric and positive definite matrices. The proposed approach has been extensively evaluated both on simulated data and real robot experiments. The performed evaluation demonstrates that beneficial properties of DMPs, such as convergence to a given goal and the possibility to change the goal during operation, apply also to the proposed formulation. © 2022, CC BY-NC-ND.

关键词： Demonstrations

Deep Active Learning with Noise Stability

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arXiv 2022年

作者： Li, Xingjian Yang, Pengkun Gu, Yangcheng Zhan, Xueying Wang, Tianyang Xu, Min Xu, Chengzhong Computational Biology Department Carnegie Mellon University United States Center for Statistical Science Tsinghua University China School of Software Tsinghua University China Department of Computer Science University of Alabama Birmingham United Kingdom State Key Lab of IOTSC University of Macau China

Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging due to the potential over-confidence of the model inference. Existing methods resort to special learning fashions (e.g. adversarial) or auxiliary models to address this challenge. This tends to result in complex and inefficient pipelines, which would render the methods impractical. In this work, we propose a novel algorithm that leverages noise stability to estimate data uncertainty. The key idea is to measure the output derivation from the original observation when the model parameters are randomly perturbed by noise. We provide theoretical analyses by leveraging the small Gaussian noise theory and demonstrate that our method favors a subset with large and diverse gradients. Our method is generally applicable in various tasks, including computer vision, natural language processing, and structural data analysis. It achieves competitive performance compared against state-of-the-art active learning baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Towards Motion Compensation in Autonomous Robotic Subretinal Injections

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arXiv 2024年

作者： Arikan, Demir Zhang, Peiyao Sommersperger, Michael Dehghani, Shervin Esfandiari, Mojtaba Taylor, Russel H. Nasseri, M. Ali Gehlbach, Peter Navab, Nassir Iordachita, Iulian Department of Computer Science Technische Universität München Munich85748 Germany the Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD United States Augenklinik und Poliklinik Klinikum Rechts der Isar der Technische Universität München München81675 Germany Technical University of Munich Munich85748 Germany

Exudative (wet) age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, typically treated with intravitreal injections. Emerging therapies, such as subretinal injections of stem cells, gene therapy, small molecules and RPE cells require precise delivery to avoid damaging delicate retinal structures. Robotic systems can potentially offer the necessary precision for these procedures. This paper presents a novel approach for motion compensation in robotic subretinal injections, utilizing real time Optical Coherence Tomography (OCT). The proposed method leverages B5-scans, a rapid acquisition of small-volume OCT data, for dynamic tracking of retinal motion along the Z-axis, compensating for physiological movements such as breathing and heartbeat. Validation experiments on ex vivo porcine eyes revealed challenges in maintaining a consistent tool-to-retina distance, with deviations of up to 200 µm for 100 µm amplitude motions and over 80 µm for 25 µm amplitude motions over one minute. Subretinal injections faced additional difficulties, with phase shifts causing the needle to move off-target and inject into the vitreous. These results highlight the need for improved motion prediction and horizontal stability to enhance the accuracy and safety of robotic subretinal procedures. © 2024, CC BY.

关键词： Stem cells

Bioluminescence tomography system for in vivo irradiation guidance

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Bioluminescence tomography system for in vivo irradiation gu...

Optical Tomography and Spectroscopy, OTS 2022

作者： Deng, Zijian Xu, Xiangkun Iordachita, Iulian Dehghani, Hamid Zhang, Bin Wong, John W. Wang, Ken Kang-Hsin Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University BaltimoreMD21287 United States Biomedical Imaging and Radiation Technology Laboratory Department of Radiation Oncology University of Texas Southwestern Medical Center DallasTX75390 United States Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom School of Biomedical Engineering Dalian University of Technology Liaoning Dalian116024 China

We constructed a bioluminescence tomography(BLT) to localize soft tissue targets for preclinical radiotherapy study. With the threshold and margin designed for target volume, BLT can provide opportunity to perform con... 详细信息

ISBN: (纸本)9781557528209

关键词： Irradiation

Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance

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arXiv 2024年

作者： Arikan, Demir Zhang, Peiyao Sommersperger, Michael Dehghani, Shervin Esfandiari, Mojtaba Taylor, Russel H. Nasseri, M. Ali Gehlbach, Peter Navab, Nassir Iordachita, Iulian Department of Computer Science Technische Universität München Munich85748 Germany Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD United States Augenklinik und Poliklinik Klinikum rechts der Isar der Technische Universität München München81675 Germany Technical University of Munich Munich85748 Germany

Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space. However, tissue deformations due to tool-tissue interactions constitute a significant challenge in autonomous iOCT-guided robotic subretinal injections. Such interactions impact correct needle positioning and procedure outcomes. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. The technique is achieved through real-time segmentation and 3D reconstruction of the surgical scene from densely sampled iOCT B-scans, which we refer to as B5scans. Using B5-scans we monitor the position of the instrument relative to a virtual target layer between the ILM and RPE. Our experiments on ex-vivo porcine eyes demonstrate dynamic adjustment of the insertion depth and overall improved accuracy in needle positioning compared to prior autonomous insertion approaches. Compared to a 35% success rate in subretinal bleb generation with previous approaches, our method reliably created subretinal blebs in 90% our experiments. The source code and data used in this study are publicly available on GitHub1 © 2024, CC BY.

关键词： Needles

Determination of Neuron Activation States Facilitated by Artificial Intelligence

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Determination of Neuron Activation States Facilitated by Art...

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Topasna, Ana Xie, Eric Park, Sammy Huang, Sophia Wu, Xiaowei Xie, Hehuang Epigenomics and Computational Biology Lab Fralin Life Sciences Institute Virginia Tech BlacksburgVA24061 United States Department of Biomedical Sciences and Pathobiology Virginia-Maryland College of Veterinary Medicine Virginia Tech BlacksburgVA24061 United States Department of Biology Department of Psychology Reed College PortlandOR97202 United States Department of Computer Science University of Virginia CharlottesvilleVA22903 United States Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA22903 United States Department of Statistics Virginia Tech BlacksburgVA24061 United States

ISBN: (纸本)9781665468190

Environmental stimuli evoke gene expression changes in neurons to achieve synaptic reconfiguration and memory formation. During the neuronal activation process, immediate early genes (IEGs) and late response genes (LRGs) have been defined according to their temporal changes in gene expression. Recent single cell studies indicated that different subtypes of neurons may have distinct sets of IEGs and LRGs in response to neuronal activation. Currently, it remains unexplored whether the gene expression patterns provided by single cell studies are sufficient to distinguish neuronal subtypes together with their activation states. In this study, we adopt Artificial Neural Network (ANN) models to predict neuron cell subtypes and activation states simultaneously based on a single cell RNA-Seq dataset generated from mouse visual cortex in response to light stimulus exposure. Four kinds of ANN architectures were designed to compare the prediction efficacies using two kinds of inputs: the full list of genes or the markers selected for neuron cell subtypes, IEGs, and LRGs. We observed that, with single-cell RNA-Seq data, neuron cell subtypes and activation states can be predicted accurately by ANN models. Moreover, compared to feeding the entire list of genes to fully connected ANNs, using selected marker genes and partially connected ANNs achieves comparable prediction accuracy with substantially improved computational efficiency. Altogether, our results demonstrated that ANNs are powerful in predicting neuron activation states and cell subtypes and may serve as a useful data mining approach to explore single cell RNA-Seq profiles. © 2022 IEEE.

关键词： RNA

Byzantine-Resilient Decentralized Stochastic Optimization with Robust Aggregation Rules

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arXiv 2022年

作者： Wu, Zhaoxian Chen, Tianyi Ling, Qing School of Computer Science and Engineering Guangdong Provincial Key Laboratory of Computational Science Sun Yat-Sen University Guangdong Guangzhou510006 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Pazhou Lab Guangdong Guangzhou510330 China

This paper focuses on decentralized stochastic optimization in the presence of Byzantine attacks. During the optimization process, an unknown number of malfunctioning or malicious workers, termed as Byzantine workers, disobey the algorithmic protocol and send arbitrarily wrong messages to their neighbors. Even though various Byzantine-resilient algorithms have been developed for distributed stochastic optimization with a central server, we show that there are two major issues in the existing robust aggregation rules when being applied to the decentralized scenario: disagreement and non-doubly stochastic virtual mixing matrix. This paper provides comprehensive analysis that discloses the negative effects of these two issues, and gives guidelines of designing favorable Byzantine-resilient decentralized stochastic optimization algorithms. Under these guidelines, we propose iterative outlier scissor (IOS), an iterative filtering-based robust aggregation rule with provable performance guarantees. Numerical experiments demonstrate the effectiveness of IOS. The code of simulation implementation is available at ***/Zhaoxian-Wu/IOS. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimization

CCGRID 2023: A Holistic Approach to Inclusion and Belonging

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CCGRID 2023: A Holistic Approach to Inclusion and Belonging

IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Beth Plale Preeti Malakar Meenakshi D'Souza Hemangee K. Kapoor Yogesh Simmhan Ilkay Altintas Manohar Swaminathan School of Informatics Computing and Engineering Indiana University Bloomington IN USA Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kalyanpur Kanpur India International Institute of Information Technology Bangalore Bengaluru Karnataka India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam India Department of Computational and Data Sciences Indian Institute of Science Bangalore India San Diego Supercomputer Center University of California San Diego MC USA Microsoft Research Lab - India Bengaluru Karnataka India

“CCGRID will act with responsibility as its primary consideration; with equity, diversity, and inclusion as its central goals.” from the CCGRID 2023 web site [1]

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Raspberry Pi Assisted Safety System for Elderly People: An Application of Smart Home 1

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Raspberry Pi Assisted Safety System for Elderly People: An A...

1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020

作者： Jan, Habab Yar, Hikmat Iqbal, Javed Farman, Haleem Khan, Zahid Koubaa, Anis Sarhad University of Science and It Department of Electrical Engineering Peshawar Pakistan Islamia College Department of Computer Science Peshawar Pakistan Robotics and Internet-of-Things Lab Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781728174075

Nowadays, the care of elderly peoples is a significant problem and needs more time and attention from the caregivers. In the elderly population, accidental fall is a common cause of injury due to the weakness or any other disease such as high temperature and blood pressure. Moreover, in developing countries, the use of natural gas for older adults is widespread for room heating. The use of a gas heater in a room can be hazardous, especially at night and it has reportedly said to turn OFF the gas heater while going to sleep. However, it is ignored quite often that results in many deaths not only to the elderly but to all age groups. To cope with these issues and to provide a safer environment, especially to the older people where the caretaker can be informed well on-time, a raspberry Pi assisted cost-effective safety system is proposed. The 3-axis accelerometer of gas and flame sensors are used to detect an event when accidental fall occurs precisely, or any gas leakage or flame is detected. The caretaker will be informed using a mobile app or text alert to take action immediately. Results demonstrate that the modules for fall and gas/open flame detection accurately detected true events by 92% and 95% respectively. The primary purpose of the proposed system is to provide a reliable, cost-effective, and efficient method for the safety of the elderly population. © 2020 IEEE.

关键词： Blood pressure