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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Luca Lach Niklas Funk Robert Haschke Séverin Lemaignan Helge Joachim Ritter Jan Peters Georgia Chalvatzaki Neuroinformatics Group Technical Faculty Bielefeld University Institut de Robòtica i Informatica Industrial CSIC- UPC Computer Science Department Technical University Darmstadt PAL Robotics Research Department: Systems AI for Robot Learning German Research Center for AI (DFKI) Hessian AI Centre for Cognitive Science

This work deals with a practical everyday problem: stable object placement on flat surfaces starting from unknown initial poses. Common object-placing approaches require either complete scene specifications or extrinsic sensor measurements, e.g., cameras, that occasionally suffer from occlusions. We propose a novel approach for stable object placing that combines tactile feedback and proprioceptive sensing. We devise a neural architecture called PlaceNet that estimates a rotation matrix, resulting in a corrective gripper movement that aligns the object with the placing surface for the subsequent object manipulation. We compare models with different sensing modalities, such as force-torque, an external motion capture system, and two classical baseline models in real-world object placing tasks with different objects. The experimental evaluation of our placing policies with a set of unseen everyday objects reveals significant generalization of our proposed pipeline, suggesting that tactile sensing plays a vital role in the intrinsic understanding of robotic dexterous object manipulation. Code, models, and supplementary videos are available on https://***/view/placing-by-touching.

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Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing

arXiv

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arXiv 2022年

作者： Lach, Luca Funk, Niklas Haschke, Robert Lemaignan, Séverin Ritter, Helge Joachim Peters, Jan Chalvatzaki, Georgia Neuroinformatics Group Technical Faculty Bielefeld University Germany Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain Computer Science Department Technical University Darmstadt Germany PAL Robotics Research Department: Systems AI for Robot Learning Germany Hessian.AI Centre for Cognitive Science

This work deals with a practical everyday problem: stable object placement on flat surfaces starting from unknown initial poses. Common object-placing approaches require either complete scene specifications or extrinsic sensor measurements, e.g., cameras, that occasionally suffer from occlusions. We propose a novel approach for stable object placing that combines tactile feedback and proprioceptive sensing. We devise a neural architecture that estimates a rotation matrix, resulting in a corrective gripper movement that aligns the object with the placing surface for the subsequent object manipulation. We compare models with different sensing modalities, such as force-torque and an external motion capture system, in real-world object placing tasks with different objects. The experimental evaluation of our placing policies with a set of unseen everyday objects reveals significant generalization of our proposed pipeline, suggesting that tactile sensing plays a vital role in the intrinsic understanding of robotic dexterous object manipulation. Code, models, and supplementary videos are available on https://***/ view/placing-by-touching. Copyright © 2022, The Authors. All rights reserved.

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The Technome - A Predictive Internal Calibration Approach for Quantitative Imaging Biomarker Research (vol 10, 1103, 2020)

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SCIENTIFIC REPORTS 2020年第1期10卷 1103-1103页

作者： Muehlberg, Alexander Katzmann, Alexander Heinemann, Volker Kaergel, Rainer Wels, Michael Taubmann, Oliver Lades, Felix Huber, Thomas Maurus, Stefan Holch, Julian Faivre, Jean-Baptiste Suehling, Michael Noerenberg, Dominik Remy-Jardin, Martine Department CT R&D Image Analytics Siemens Healthineers Forchheim 91301 German Department CT R&D Image Analytics Siemens Healthineers Forchheim 91301 Germany Neuroinformatics and Cognitive Robotics Lab University of Technology Ilmenau 98693 Germany Department of Medical Oncology University Hospital Großhadern LMU Munich 81377 Germany Comprehensive Cancer Center University Hospital Großhadern LMU Munich 81377 Germany Department of Radiology University Hospital Großhadern LMU Munich 81377 Germany Department of Thoracic Imaging CHRU et Universite de Lille 2 Hospital Calmette Lille 59037 France

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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TumorEncode - Deep Convolutional Autoencoder for Computed Tomography Tumor Treatment Assessment

TumorEncode - Deep Convolutional Autoencoder for Computed To...

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Katzmann, Alexander Mühlberg, Alexander Sühling, Michael Nörenberg, Dominik Holch, Julian Walter Groß, And Horst-Michael Siemens Healthcare GmbH Department CT R and D Image Analytics Forchheim91301 Germany University of Technology Ilmenau Neuroinformatics and Cognitive Robotics Lab Ilmenau98693 Germany Department of Radiology University Hospital Großhadern Ludwig-Maximilians-University Munich Munich81377 Germany Department of Internal Medicine III Comprehensive Cancer Center University Hospital Großhadern Ludwig-Maximilians-University Munich Munich81377 Germany

ISBN: (纸本)9781509060146

In tumor therapy, estimating tumor growth is crucial to get an early information regarding tumor therapy response and, if neccessary, adapt therapy. We propose a novel deep learning based algorithm using deep convolutional sparse autoencoders to find a minimal representation of tumor shape and texture for colorectal liver metastases. Furthermore, we provide a prediction of future lesion growth based on single slice CT tumor images which prospectively can be used as a prognosis for physicians. The state of the art in tumor treatment assessment for solid tumors mainly uses tumor diameter in single CT slices as the treatment response criterion (RECIST). However, whereas the correlation between RECIST and final treatment outcome was shown to be significant, its effect size is still limited. With our approach we achieve a Matthews correlation coefficient of 52.0% in predicting tumor treatment response compared to 28.2% with radiologic assessment, as well as an AUC of 0.814 opposed to 0.698. © 2018 IEEE.

关键词： Tumors

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TumorEncode - Deep Convolutional Autoencoder for Computed Tomography Tumor Treatment Assessment

TumorEncode - Deep Convolutional Autoencoder for Computed To...

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International Joint Conference on Neural Networks

作者： Alexander Katzmann Alexander Muhlberg Michael Suhling Dominik Norenberg Julian Walter Holch Horst-Michael Gross Department CT R&D Image Analytics Siemens Healthcare GmbH Forchheim Germany Department of Radiology University Hospital Großhadern Ludwig-Maximilians-University Munich Munich Germany Department of Internal Medicine III University Hospital Großhadern Ludwig-Maximilians-University Munich Munich Germany Neuroinformatics and Cognitive Robotics Lab University of Technology Ilmenau Ilmenau 98693 Germany

关键词： Lesions Computed tomography Cancer Liver Task analysis

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Publisher Correction: The Technome - A Predictive Internal Calibration Approach for Quantitative Imaging Biomarker Research

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Scientific reports 2020年第1期10卷 11442页

作者： Alexander Mühlberg Alexander Katzmann Volker Heinemann Rainer Kärgel Michael Wels Oliver Taubmann Félix Lades Thomas Huber Stefan Maurus Julian Holch Jean-Baptiste Faivre Michael Sühling Dominik Nörenberg Martine Rémy-Jardin Department CT R&D Image Analytics Siemens Healthineers 91301 Forchheim Germany. alexander-muehlberg@***. Department CT R&D Image Analytics Siemens Healthineers 91301 Forchheim Germany. Neuroinformatics and Cognitive Robotics Lab University of Technology 98693 Ilmenau Germany. Department of Medical Oncology University Hospital Großhadern LMU 81377 Munich Germany. Comprehensive Cancer Center University Hospital Großhadern LMU 81377 Munich Germany. Department of Radiology University Hospital Großhadern LMU 81377 Munich Germany. Department of Thoracic Imaging CHRU Et Universite de Lille 2 Hospital Calmette 59037 Lille France.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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2022 Roadmap on Neuromorphic Computing and Engineering

arXiv

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arXiv 2021年

作者： Christensen, Dennis Valbjørn Dittmann, Regina Linares-Barranco, Bernabé Sebastian, Abu Le Gallo, Manuel Redaelli, Andrea Slesazeck, Stefan Mikolajick, Thomas Spiga, Sabina Menzel, Stephan Valov, Ilia Milano, Gianluca Ricciardi, Carlo Liang, Shi-Jun Miao, Feng Lanza, Mario Quill, Tyler J. Keene, Scott T. Salleo, Alberto Grollier, Julie Marković, Danijela Mizrahi, Alice Yao, Peng Yang, J. Joshua Indiveri, Giacomo Strachan, John Paul Datta, Suman Vianello, Elisa Valentian, Alexandre Feldmann, Johannes Li, Xuan Pernice, Wolfram H.P. Bhaskaran, Harish Furber, Steve Neftci, Emre Scherr, Franz Maass, Wolfgang Ramaswamy, Srikanth Tapson, Jonathan Panda, Priyadarshini Kim, Youngeun Tanaka, Gouhei Thorpe, Simon Bartolozzi, Chiara Cleland, Thomas A. Posch, Christoph Liu, Shih-Chii Panuccio, Gabriella Mahmud, Mufti Mazumder, Arnab Neelim Hosseini, Morteza Mohsenin, Tinoosh Donati, Elisa Tolu, Silvia Galeazzi, Roberto Christensen, Martin Ejsing Holm, Sune Ielmini, Daniele Pryds, N. Department of Energy Conversion and Storage Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Peter Gruenberg Institute 7 Forschungszentrum Juelich GmbH Juelich52425 Germany JARA-FIT RWTH Aachen University Aachen52056 Germany CSIC Universidad de Sevilla Seville41092 Spain IBM Research Zurich Switzerland STMicroelectronics Agrate Italy Dresden01187 Germany Institute of Semiconductors and Microsystems TU Dresden Dresden Germany CNR-IMM Unit of Agrate Brianza via C. Olivetti 2 MB Agrate Brianza Italy Juelich Germany Torino Italy Department of Applied Science and Technology Politecnico di Torino Torino Italy National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China Thuwal23955-6900 Saudi Arabia Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Unité Mixte de Physique CNRS Thales Université Paris-Saclay Palaiseau91767 France Electrical and Computer Engineering Department University of Southern California Los AngelesCA United States Institute of Neuroinformatics University of Zurich ETH Zurich Switzerland Hewlett Packard Laboratories Hewlett Packard Enterprise San JoseCA United States Department of Electrical Engineering University of Notre Dame Notre DameIN United States CEA LETI Université Grenoble Alpes Grenoble France CEA LIST Université Grenoble Alpes Grenoble France Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom Institute of Physics University of Münster Heisenbergstr. 11 Heisenbergstr Münster48149 Germany Center for Soft Nanoscience University of Münster Münster48149 Germany The University of Manchester United Kingdom Department of Cognitive Sciences University of California Irvine IrvineCA United States Institute of Theoretica

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community. © 2021,

关键词： Data transfer

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Learning to navigate through crowded environments

Learning to navigate through crowded environments

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IEEE International Conference on robotics and Automation (ICRA)

作者： Peter Henry Christian Vollmer Brian Ferris Dieter Fox Department of Computer Science & Engineering University of Washington Seattle WA USA Neuroinformatics and Cognitive Robotics Laboratory Ilmenau University of Technology Germany

The goal of this research is to enable mobile robots to navigate through crowded environments such as indoor shopping malls, airports, or downtown side walks. The key research question addressed in this paper is how to learn planners that generate human-like motion behavior. Our approach uses inverse reinforcement learning (IRL) to learn human-like navigation behavior based on example paths. Since robots have only limited sensing, we extend existing IRL methods to the case of partially observable environments. We demonstrate the capabilities of our approach using a realistic crowd flow simulator in which we modeled multiple scenarios in crowded environments. We show that our planner learned to guide the robot along the flow of people when the environment is crowded, and along the shortest path if no people are around.

关键词： Navigation Learning Cost function Mobile robots Airports Robot sensing systems Gaussian processes robotics and automation USA Councils Computer science

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