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IEEE International Conference on Systems, Man, and Cybernetics

作者： Michael Volkhardt Friederike Schneemann Horst-Michael Gross Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab 98684 Ilmenau Germany

ISBN: (纸本)9781479906505

Falling down and not managing to get up again is one of the main concerns of elderly people living alone in their home. Robotic assistance for the elderly promises to have a great potential of detecting these critical situations and calling for help. This paper presents a feature-based method to detect fallen people on the ground by a mobile robot equipped with a Kinect sensor. Point clouds are segmented, layered and classified to detect fallen people, even under occlusions by parts of their body or furniture. Different features, originally from pedestrian and object detection in depth data, and different classifiers are evaluated. Evaluation was done using data of 12 people lying on the floor. Negative samples were collected from objects similar to persons, two tall dogs, and five real apartments of elderly people. The best feature-classifier combination is selected to built a robust system to detect fallen people.

关键词： Fallen person detection Kinect mobile robot 3D depth data

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MDP-based Motion Planning for Grasping in Dynamic Scenarios

MDP-based Motion Planning for Grasping in Dynamic Scenarios

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European Conference on Mobile Robots (ECMR)

作者： Steffen Müller Benedict Stephan Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Technische Universität Ilmenau Ilmenau Germany

Path planning for robotic manipulation is a well understood topic as long as the execution of the plan takes place in a static scene. Unfortunately, for applications involving human interaction partners a dynamic obstacle configuration has to be considered. Furthermore, if it comes to grasping objects from a human hand, there is not a single goal position and the optimal grasping configuration may change during the execution of the grasp movement. This makes a continuous re-planning in a loop necessary. Besides efficiency and security concerns, such periodic planning raises the additional requirement of consistency, which is hard to achieve with traditional sampling based planners. We present an online capable planner for continuous control of a robotic grasp task. The planner additionally is able to resolve multiple possible grasp poses and additional goal functions by applying an MDP-like optimization of future rewards. Furthermore, we present a heuristic for setting edges in a probabilistic roadmap graph that improves the connectivity and keeps edge count low.

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Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis

Efficient RGB-D Semantic Segmentation for Indoor Scene Analy...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel Seichter Mona Köhler Benjamin Lewandowski Tim Wengefeld Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Technische Universität Ilmenau Ilmenau Germany

Analyzing scenes thoroughly is crucial for mobile robots acting in different environments. Semantic segmentation can enhance various subsequent tasks, such as (semantically assisted) person perception, (semantic) free space detection, (semantic) mapping, and (semantic) navigation. In this paper, we propose an efficient and robust RGB-D segmentation approach that can be optimized to a high degree using NVIDIA TensorRT and, thus, is well suited as a common initial processing step in a complex system for scene analysis on mobile robots. We show that RGB-D segmentation is superior to processing RGB images solely and that it can still be performed in real time if the network architecture is carefully designed. We evaluate our proposed Efficient Scene Analysis Network (ESANet) on the common indoor datasets NYUv2 and SUNRGB-D and show that we reach state-of-the-art performance while enabling faster inference. Furthermore, our evaluation on the outdoor dataset Cityscapes shows that our approach is suitable for other areas of application as well. Finally, instead of presenting benchmark results only, we also show qualitative results in one of our indoor application scenarios.

关键词： Image segmentation Image analysis Navigation Semantics Network architecture Real-time systems Decoding

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MIRA - Middleware for Robotic Applications

MIRA - Middleware for Robotic Applications

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IEEE/RSJ International Conference on Intelligent robotics and Systems

作者： Erik Einhorn Tim Langner Ronny Stricker Christian Martin Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Germany MetraLabs Robotics GmbH Ilmenau Germany

ISBN: (纸本)9781467317375

In this paper, we present MIRA, a new middleware for robotic applications. It is designed for use in real-world applications and for research and teaching. In comparison to many other existing middlewares, MIRA employs novel techniques for communication that are described in this paper. Moreover, we present benchmarks that analyze the performance of the most commonly used middlewares ROS, Yarp, LCM, Player, Urbi, and MOOS. Using these benchmarks, we can show that MIRA outperforms the other middlewares in terms of latency and computation time.

关键词： MIRA ROS LCM Lowest common multiple Middleware MIRA VARIABLE STARS ROS1 gene BENCHMARKS computational time

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Comparison of long-term adaptivity for neural networks

Comparison of long-term adaptivity for neural networks

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22nd International Conference on Artificial Neural Networks, ICANN 2012

作者： Steege, Frank-Florian Groß, Horst-Michael Ilmenau Technical University Neuroinformatics and Cognitive Robotics Lab. P.O. Box 100565 Ilmenau D-98684 Germany Powitec Intelligent Technologies GmbH Essen-Kettwig 45219 Germany

ISBN: (纸本)9783642332654

Neural Networks can be used for the prognosis of important quality measures in industrial processes to complement or reduce costly laboratory analysis. Problems occur if the system dynamics change over time (concept drift). We survey different approaches to handle concept drift and to ensure good prognosis quality over long time ranges. Two main approaches - data accumulation and ensemble learning - are explained and implemented. We compare the concepts on artificial datasets and on industrial data from three cement production plants and analyse strengths and weaknesses of different approaches. © 2012 Springer-Verlag.

关键词： Industrial plants

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Few-Shot Object Detection: A Comprehensive Survey

arXiv

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arXiv 2021年

作者： Köhler, Mona Eisenbach, Markus Gross, Horst-Michael Neuroinformatics and Cognitive Robotics Lab. Ilmenau University of Technology Ilmenau98693 Germany

Humans are able to learn to recognize new objects even from a few examples. In contrast, training deep-learning-based object detectors requires huge amounts of annotated data. To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain. In this survey, we provide an overview of the state of the art in few-shot object detection. We categorize approaches according to their training scheme and architectural layout. For each type of approaches, we describe the general realization as well as concepts to improve the performance on novel categories. Whenever appropriate, we give short takeaways regarding these concepts in order to highlight the best ideas. Eventually, we introduce commonly used datasets and their evaluation protocols and analyze reported benchmark results. As a result, we emphasize common challenges in evaluation and identify the most promising current trends in this emerging field of few-shot object detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis

arXiv

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arXiv 2020年

作者： Seichter, Daniel Köhler, Mona Lewandowski, Benjamin Wengefeld, Tim Gross, Horst-Michael Neuroinformatics and Cognitive Robotics Lab Technische Universität Ilmenau Ilmenau98693 Germany

关键词： Semantic Segmentation

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Realization and User Evaluation of a Companion Robot for People with Mild cognitive Impairments

Realization and User Evaluation of a Companion Robot for Peo...

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IEEE International Conference on robotics and Automation

作者： Ch. Schroeter S. Mueller M. Volkhardt E. Einhorn C. Huijnen H. van den Heuvel A. van Berlo A. Bley H. M. Gross Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Stichting Smart Homes MetraLabs GmbH

ISBN: (纸本)9781467356404

This paper presents results of user evaluations with a socially assistive robot companion for older people suffering from mild cognitive impairment (MCI) and living (alone) at home. Within the European FP7 project "CompanionAble" (2008-2012) [1], we developed assistive technologies combining a mobile robot and smart environment with the aim to support these people and assist them living in their familiar home environment. For a final evaluation, user experience studies were conducted with volunteer users who were invited to a test home where they lived and freely used the robot and integrated system over a period of two days. Services provided by the companion robot include reminders of appointments (pre-defined or added by the users themselves or their informal carer) as well as frequent recommendations to specific activities, which were listed e.g. by their family carers. Furthermore, video contact with relatives and friends, a cognitive stimulation game designed especially to counter the progress of cognitive impairments, and the possibility to store personal items with the robot are offered. Recognition of the user entering or leaving the home is triggering situation specific reminders like agenda items due during the (expected) absence, missed calls or items not to be forgotten. Continuing our previous work published in [2], this paper presents detailed description of the implemented assistive functions and results of user studies conducted during April and May 2012 in the smart house of the Dutch project partner Smart Homes in Eindhoven, The Netherlands.

关键词： Mild cognitive Impairment Robot intelligent buildings Companions Self-Help Devices Domestic architecture Use Studies Living Alone home environment Persons Appointments cognitive Decline Caregivers Mobile robots

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Agent self-assessment: Determining policy quality without execution

Agent self-assessment: Determining policy quality without ex...

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IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

作者： Hans, Alexander Duell, Siegmund Udluft, Steffen Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Ilmenau Germany Machine Learning Group Berlin Institute of Technology Berlin Germany Intelligent Systems and Control Siemens AG Corporate Technology Munich Munich Germany

ISBN: (纸本)9781424498888

With the development of data-efficient reinforcement learning (RL) methods, a promising data-driven solution for optimal control of complex technical systems has become available. For the application of RL to a technical system, it is usually required to evaluate a policy before actually applying it to ensure it operates the system safely and within required performance bounds. In benchmark applications one can use the system dynamics directly to measure the policy quality. In real applications, however, this might be too expensive or even impossible. Being unable to evaluate the policy without using the actual system hinders the application of RL to autonomous controllers. As a first step toward agent self-assessment, we deal with discrete MDPs in this paper. We propose to use the value function along with its uncertainty to assess a policy's quality and show that, when dealing with an MDP estimated from observations, the value function itself can be misleading. We address this problem by determining the value function's uncertainty through uncertainty propagation and evaluate the approach using a number of benchmark applications. © 2011 IEEE.

关键词： Reinforcement learning

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Robotic Companions for Older People: A Case Study in the Wild

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Studies in Health Technology and Informatics 2015年 219卷 147-152页

作者： Doering, Nicola Richter, Katja Gross, Horst-Michael Schroeter, Christof Mueller, Steffen Volkhardt, Michael Scheidig, Andrea Debes, Klaus TU Ilmenau Media Psychology and Media Design Group Germany TU Ilmenau Neuroinformatics and Cognitive Robotics Lab Germany

ISBN: (纸本)9781614995944

Older people tend to have difficulties using unknown technical devices and are less willing to accept technical shortcomings. Therefore, a robot that is supposed to support older people in managing daily life has to adapt to the users' needs and capabilities that are very heterogeneous within the target group. The aim of the presented case study was to provide in-depth insights on individual usage patterns and acceptance of a mobile service robot in real live environments (i.e. in the users' homes). Results from three cases (users aged 67, 78 and 85 living in their own apartments) are reported. Findings on usability and user experience illustrate that the robot has considerable potential to be accepted to support daily living at home. © 2015 The authors and IOS Press. All rights reserved.

关键词： Robots

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