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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Nikolas Dahn Stefan Fuchs Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Ilmenau Germany Honda Research Institute Offenbach 63073 Germany Honda Research Institute Offenbach Germany

ISBN: (纸本)9781538679814

Situation Awareness is a prominent concept in the human factors community. It is used to analyze and eliminate common sources of human errors in complex tasks and has seen wide-spread use in many fields, such as aviation, health care or ergonomics. Humans who are situation aware are able to reliably generate competent performance, a skill that is also highly desired for other autonomous agents. Yet, the concept has seen only limited use in robotics. We attest this to a lack of clear definitions which would allow assessing an artificial agents capacity for Situation Awareness. Our major contribution is an application-agnostic definition of the terminology and the processes involved in acquiring Situation Awareness. By integrating our definitions into the perception-action-cycle we provide a connection to the agent's observable behavior. Our second major contribution is a way to estimate, whether an agent has lost Situation Awareness based on surprise. This measure can be used online and does not require explicit or implicit knowledge of the task. We evaluate our concept on a physical workspace built for abstract Human- Robot-Cooperation scenarios.

关键词： Robot sensing systems Task analysis Autonomous agents Human factors Cognition Safety

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The Markov decision process extraction network

The Markov decision process extraction network

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18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010

作者： Duell, Siegmund Hans, Alexander Udluft, Steffen Siemens AG Corporate Research and Technologies Learning Systems Otto-Hahn-Ring 6 D-81739 Munich Germany Berlin University of Technology Machine Learning Franklinstr. 28-29 D-10587 Berlin Germany Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab P.O.Box 100565 D-98684 Ilmenau Germany

ISBN: (纸本)2930307102

This paper presents the Markov decision process extraction network, which is a data-efficient, automatic state estimation approach for discrete-time reinforcement learning (RL) based on recurrent neural networks. The architecture is designed to model the minimal relevant dynamics of an environment, capable of condensing large sets of continuous observables to a compact state representation and excluding irrelevant information. To the best of our knowledge, it is the first approach published to automatically extract minimal relevant aspects of the dynamics from observations to model a Markov decision process, suitable for RL, without requiring special knowledge of the regarded environment. The capabilities of the neural state estimation approach are evaluated using the cart-pole problem and standard table-based policy iteration.

关键词： Markov processes

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How to Get Pavement Distress Detection Ready for Deep Learning? A Systematic Approach

How to Get Pavement Distress Detection Ready for Deep Learni...

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International Joint Conference on Neural Networks

作者： Markus Eisenbach Ronny Stricker Daniel Seichter Karl Amende Klaus Debes Maximilian Sesselmann Dirk Ebersbach Ulrike Stoeckert Horst-Michael Gross Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab 98684 Ilmenau Germany LEHMANN + PARTNER GmbH 99086 Erfurt Germany German Federal Road Research Institute (BASt) 51427 Bergisch Gladbach Germany

ISBN: (纸本)9781509061839

Road condition acquisition and assessment are the key to guarantee their permanent availability. In order to maintain a country's whole road network, millions of high-resolution images have to be analyzed annually. Currently, this requires cost and time excessive manual labor. We aim to automate this process to a high degree by applying deep neural networks. Such networks need a lot of data to be trained successfully, which are not publicly available at the moment. In this paper, we present the GAPs dataset, which is the first freely available pavement distress dataset of a size, large enough to train high-performing deep neural networks. It provides high quality images, recorded by a standardized process fulfilling German federal regulations, and detailed distress annotations. For the first time, this enables a fair comparison of research in this field. Furthermore, we present a first evaluation of the state of the art in pavement distress detection and an analysis of the effectiveness of state of the art regularization techniques on this dataset.

关键词： Roads Cameras Neural networks Surface treatment Inspection labeling Surface resistance

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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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StickyPillars: Robust and efficient feature matching on point clouds using graph neural networks

arXiv

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arXiv 2020年

作者： Fischer, Kai Simon, Martin Ölsner, Florian Milz, Stefan Groß, Horst-Michael Mäder, Patrick Valeo Schalter und Sensoren GmbH Kronach Germany Spleenlab GmbH Saalburg-Ebersdorf Germany Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Germany Software Engineering for Safety-Critical Systems Ilmenau University of Technology Germany

Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initialization, insufficient overlap or in the presence of dynamic objects. Modern deep learning based registration approaches present much better results, but suffer from a heavy run-time. We overcome these drawbacks by introducing StickyPillars, a fast, accurate and extremely robust deep middle-end 3D feature matching method on point clouds. It uses graph neural networks and performs context aggregation on sparse 3D key-points with the aid of transformer based multi-head self and cross-attention. The network output is used as the cost for an optimal transport problem whose solution yields the final matching probabilities. The system does not rely on hand crafted feature descriptors or heuristic matching strategies. We present state-of-art art accuracy results on the registration problem demonstrated on the KITTI dataset while being four times faster then leading deep methods. Furthermore, we integrate our matching system into a LiDAR odometry pipeline yielding most accurate results on the KITTI odometry dataset. Finally, we demonstrate robustness on KITTI odometry. Our method remains stable in accuracy where state-of-the-art procedures fail on frame drops and higher speeds. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Recurrent neural state estimation in domains with long-term dependencies 20th

Recurrent neural state estimation in domains with long-term ...

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20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012

作者： Duell, Siegmund Weichbrodt, Lina Hans, Alexander Udluft, Steffen Siemens AG Corporate Technology Intelligent Systems and Control Otto-Hahn-Ring 6 Munich81739 Germany Berlin University of Technology Machine Learning Franklinstr. 28-29 Berlin10587 Germany Otto-von-Guericke-University Magdeburg P.O.Box 4120 Magdeburg39016 Germany Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab P.O.Box 100565 Ilmenau98684 Germany

ISBN: (纸本)9782874190490

This paper presents a state estimation approach for reinforcement learning (RL) of a partially observable Markov decision process. It is based on a special recurrent neural network architecture, the Markov decision process extraction network with shortcuts (MPEN-S). In contrast to previous work regarding this topic, we address the problem of long-term dependencies, which cause major problems in many real-world applications. The architecture is designed to model the reward-relevant dynamics of an environment and is capable to condense large sets of continuous observables to a compact Markovian state representation. The resulting estimate can be used as input for RL methods that assume the underlying system to be a Markov decision process. Although the approach was developed with RL in mind, it is also useful for general prediction tasks. © 2012, *** publication. All rights reserved.

关键词： State estimation

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Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation

arXiv

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arXiv 2022年

作者： Sämann, Timo Hammam, Ahmed Mostafa Bursuc, Andrei Stiller, Christoph Groß, Horst-Michael Valeo Schalter und Sensoren GmbH Germany Stellantis Opel Automobile GmbH Germany Valeo.ai France Institute of Measurement and Control Systems Karlsruhe Institute of Technology Germany Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab Germany

Averaging predictions of a deep ensemble of networks is a popular and effective method to improve predictive performance and calibration in various benchmarks and Kaggle competitions. However, the runtime and training cost of deep ensembles grow linearly with the size of the ensemble, making them unsuitable for many applications. Averaging ensemble weights instead of predictions circumvents this disadvantage during inference and is typically applied to intermediate checkpoints of a model to reduce training cost. Albeit effective, only few works have improved the understanding and the performance of weight averaging. Here, we revisit this approach and show that a simple weight fusion (WF) strategy can lead to a significantly improved predictive performance and calibration. We describe what prerequisites the weights must meet in terms of weight space, functional space and loss. Furthermore, we present a new test method (called oracle test) to measure the functional space between weights. We demonstrate the versatility of our WF strategy across state of the art segmentation CNNs and Transformers as well as real world datasets such as BDD100K and Cityscapes. We compare WF with similar approaches and show our superiority for in- and out-of-distribution data in terms of predictive performance and calibration. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

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A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots

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SENSORS 2020年第3期20卷 722-722页

作者： Mueller, Steffen Wengefeld, Tim Trinh, Thanh Quang Aganian, Dustin Eisenbach, Markus Gross, Horst-Michael Neuroinformatics and Cognitive Robotics Lab of Technische Universität Ilmenau 98684 Ilmenau Germanyed.uanemli-ut@hnirt.hnaht-gnauq (T.Q.T.) ed.uanemli-ut@nainaga.nitsud (D.A.) ed.uanemli-ut@hcabnesie.sukram (M.E.)

In order to meet the increasing demands of mobile service robot applications, a dedicated perception module is an essential requirement for the interaction with users in real-world scenarios. In particular, multi sensor fusion and human re-identification are recognized as active research fronts. Through this paper we contribute to the topic and present a modular detection and tracking system that models position and additional properties of persons in the surroundings of a mobile robot. The proposed system introduces a probability-based data association method that besides the position can incorporate face and color-based appearance features in order to realize a re-identification of persons when tracking gets interrupted. The system combines the results of various state-of-the-art image-based detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robot's user, even in complex, cluttered environments with long-lasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearance-based re-identification are combined. The tracking system was applied in a real world application with a mobile rehabilitation assistant robot in a public hospital. The estimated states of persons are used for the user-centered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments.

关键词： multi modal person tracking sensor fusion user centered robot navigation

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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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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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