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检索条件"机构=Munich School of Robotics and Machine Intelligence and Robotics and System Intelligence"

共 224 条记录，以下是141-150 订阅

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Haptic Dataset Augmentation with Subjective QoE Labels using Conditional Generative Adversarial Network

Haptic Dataset Augmentation with Subjective QoE Labels using...

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

作者： Zican Wang Xiao Xu Dong Yang Zhenyu Wang Sarah Shtaierman Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Technical University of Munich School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Centre for Tactile Internet with Human-in-the-Loop (CeTI) TU Dresden

This paper proposes a novel Generative Adversarial Network (GAN)-based strategy to augment subjective haptic Quality of Experience (QoE) datasets for bilateral teleoperation with haptic feedback without conducting time-consuming subjective experiments. In our previous work, we proposed a multi-assessment fusion approach to predict subjective haptic quality using a collection of objective metrics. This method requires a sufficiently large haptic dataset with QoE labels. The proposed generative approach automatically expands the existing haptic quality dataset by combining a modified conditional GAN (CGAN) and Style GAN (StyleGAN) architecture. The most important feature of our method is that it learns from the labeled training data and focuses on synthesizing signals with artifacts according to new input labels containing the QoE score, time delay, control method, and data reduction information. Extensive experiments are conducted to validate the suitability of the expanded dataset. The results show that our approach is able to generate new data, which match the label and signal distribution of the original data with categorical rank and linear correlation of over 0.85.

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Algorithm to Technology Co-Optimization for CiM-Based Hyperdimensional Computing

Algorithm to Technology Co-Optimization for CiM-Based Hyperd...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Mahta Mayahinia Simon Thomann Paul R. Genssler Christopher Münch Hussam Amrouch Mehdi B. Tahoori Department of Computer Science and Informatics Karlsruhe Institute of Technology Karlsruhe Germany Chair of AI Processor Design TUM School of Computation Information and Technology Technical University of Munich Munich Institute of Robotics and Machine Intelligence Munich Germany Semiconductor Test and Reliability University of Stuttgart Stuttgart Germany

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Hyperdimensional computing (HDC) has been recognized as an efficient machine learning algorithm in recent years. Robustness against noise and simple computational operations, while being limited by the memory bandwidth, make it a perfect fit for the concept of computation in memory (CiM) with emerging nonvolatile memory (NVM) technologies. For an HDC accelerator based on NVM-CiM, there are different parameters from the algorithm all the way down to the technology that interact with each other and affect the overall inference accuracy as well as the energy efficiency of the accelerator. Therefore, in this paper, we propose, for the first time, a full-stack co-optimization method and use it to design an HDC accelerator based on NVM-based content addressable memory (CAM). By incorporating the device manufacturing variability and co-optimizing the algorithm and hardware design, HDC inference on our proposed NVM-based CiM accelerator can reduce the energy consumption by 3.27x, while compared to the purely software-based implementation, the inference accuracy loss is merely 0.125%.

关键词： Associative memory Energy consumption machine learning algorithms Nonvolatile memory Noise Inference algorithms Hardware

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Ground-optimized SLAM with Hierarchical Loop Closure Detection in Large-scale Environment

Ground-optimized SLAM with Hierarchical Loop Closure Detecti...

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International Conference on Intelligent Transportation

作者： Huilin Yin Mina Sun Linchuan Zhang Jun Yan Johannes Betz College of Electronics and Information Engineering Tongji University Shanghai China Professorship of Autonomous Vehicle Systems TUM School of Engineering and Design Technical University Munich Garching Germany Munich Institute of Robotics and Machine Intelligence (MIRMI)

Accurate and reliable localization and mapping are crucial prerequisites for autonomous driving to achieve path planning. However, in large-scale complex dynamic environments, the traditional LiDAR loop closure detection methods that rely on radius search can easily result in false negatives, leading to the inability to correct accumulated errors effectively. To address this issue, a hierarchical LiDAR descriptor loop closure detection strategy is proposed in this paper, which detects invalid loop closures and has good viewpoint invariance. We integrate this strategy into an advanced LiDAR Inertial tightly coupled SLAM framework. In addition, to reduce the drift in the vertical direction during mapping, we introduce a ground marking algorithm and construct corresponding ground constraints in the back-end optimization. Our proposed method is evaluated on the MulRan dataset, and the experimental results show that our method could achieve lower accumulated errors than competing methods.

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OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

OCTOPUS: Optimized Cross-border TeleOperated Medicine Pourin...

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IEEE International Conference on Communications (ICC)

作者： Edwin Babaians Praveen Gorla Serkut Ayvaşık Jan Plachý Zdenek Becvar Wolfgang Kellerer Eckehard Steinbach Chair of Media Technology School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Dept. of Telecommunication Engineering Faculty of Electrical Engineering Czech Technical University Prague Chair of Communication Networks School of Computation Information and Technology Technical University of Munich Germany

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities - Prague and munich. We focus on the robot's ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system's holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial.

关键词： 5G mobile communication Urban areas Medical services Streaming media User experience Haptic interfaces Task analysis

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A robotics Perspective on Experimental Injury Biomechanics of Human Body Upper Extremities

A Robotics Perspective on Experimental Injury Biomechanics o...

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IEEE International Conference on intelligence and Safety for robotics (ISR)

作者： Mazin Hamad Alexander Kurdas Saeed Abdolshah Sami Haddadin Institute of Robotics and Systems Intelligence Munich School of Robotics and Machine Intelligence Technische Universität München (TUM) Germany

ISBN: (纸本)9781665438629;9781665411486

Achieving a better understanding of injury mechanisms and significant factors affecting safety in robotics requires large amounts of ground-truth, medically classified biomechanical information. Representing impact data-sets as relationships between input parameters (reflected mass, velocity, injury) for different impact curvatures and test setups versus injury was previously proven to be very efficient for ensuring human safety through real-time robot control algorithms. However, such a data-driven safety framework requires enough data-sets to cover all practical collision incidents. In this work, resulting data-sets from our extensive literature review process on the human body upper extremities are summarized. We also adapt our previously developed robotics-compatible representations in terms of standardized testing setups, impact scenarios, and collision cases to distinctly categorize impact-testing experiments on the human body upper extremities. Each category can then be differentiated by a minimal representative set of test arrangements to unify the description, storage, reporting, and comparison of experimental settings and results. This way, every new impact test/incident can be easily categorized and compared against others in the developed digital database.

关键词： Biomechanics Databases Conferences Robot control Real-time systems Safety Collision avoidance

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Experimental Injury Biomechanics of Human Body Upper Extremities: Anatomy, Injury Severity Classification, and Impact Testing Setups

Experimental Injury Biomechanics of Human Body Upper Extremi...

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IEEE International Conference on intelligence and Safety for robotics (ISR)

Understanding the human body's anatomical aspects helps capture key insights and gather increased information on the underlying human injury mechanisms, injury probability, and tolerance limits. Such knowledge, together with capable robots integrating safety-driven motion planning and control frameworks, enables the development of effective countermeasures for human arm/hand injury in robotics. In this work, thorough treatment for the anatomy, injury types, and severity classifications of human upper body extremities are provided. We also describe various impact testing setups and injury occurrence mechanisms encountered in sports, biomechanics, and forensics literature. The gathered insights provide solid grounds for designing/planning interactive impact setups and robot tasks in a well-informed way regarding the injury biomechanics of the human body upper extremities and their tolerances.

关键词： Biomechanics Solids Safety Planning Task analysis Injuries Robots

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Improving Equine Intramedullary Nail Osteosynthesis via Fracture Adjacent Polymer Reinforcement

Improving Equine Intramedullary Nail Osteosynthesis via Frac...

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作者： Lang, Jan J. Baylacher, Veronika Micheler, Carina M. Wilhelm, Nikolas J. Hinterwimmer, Florian Schwaiger, Benedikt Barnewitz, Dirk von Eisenhart-Rothe, Rüdiger Grosse, Christian U. Burgkart, Rainer Munich Germany Department of Orthopedics and Sports Orthopedics School of Medicine TUM Munich Germany Institute for Machine Tools and Industrial Management School of Engineering and Design TUM Munich Germany Munich Institute of Robotics and Machine Intelligence Department of Electrical and Computer Engineering TUM Munich Germany School of Medicine Institute for AI and Informatics in Medicine TUM Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine TUM Munich Germany Research Centre of Medical Technology and Biotechnology Bad Langensalza Germany Non-destructive Testing Department of Mechanical Engineering TUM Munich Germany

Introduction: Osteosynthesis of the equine femur is still a challenge for veterinary medicine. Even though intramedullary fracture fixation is possible nowadays, the varying geometry of the medullary cavity along the bone axis is a critical factor. Limited contact area between implant and bone can cause insufficient primary stability. In this study, it was investigated whether the osteosynthesis stability can be improved with a form-adaptive reinforcement for the diaphyseal part of the proximal fragment. Material and Methods: Eight equine femora were fitted with intramedullary nail osteosynthesis and analyzed by 4-point bending. Virtual position planning of the ex-vivo implantation using CT-data increased comparability. For five femora the proximal fragment was reinforced with a flexible polymer mixture. Longterm stability was tested via cyclic loading. Bending stiffness and its development due to cyclic loading was evaluated before and after reinforcement procedure. Finally, load-to-failure was tested in the same setup. Results and Discussion: The application of the polymer reinforcement increased the maximum torque in the load-to-failure measurement by 26%. Bending stiffness was not affected in the measured loading range by the reinforcement. Cyclic loading increased bending stiffness for a conditioned state but showed to be reversible for the most part. Conclusion: The fracture adjacent reinforcement showed to be beneficial to the osteosynthesis stability, but further investigation is necessary for surgical application. © 2022 The Author(s), published by De Gruyter.

关键词： Reinforcement

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CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

CITR: A Coordinate-Invariant Task Representation for Robotic...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： Learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

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Self-Supervised Object Recognition Based on Repeated Re-Capturing of Dynamic Indoor Environments

Self-Supervised Object Recognition Based on Repeated Re-Capt...

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IEEE International Symposium on Multimedia

作者： Martin Piccolrovazzi Michael G. Adam Sebastian Eger Marsil Zakour Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Technical University of Munich School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany

ISBN: (纸本)9781665471732

Capturing a digital replica of an environment using hand held devices or mobile mapping systems has become increasingly easy in recent years. However, leveraging large amounts of data for various semantic applications is usually impaired by a costly data annotation process. In this paper, we investigate the topic of object recognition in indoor environments without supervision. We approach the problem from a remapping perspective, where we capture RGB images from the same environment at different times and use the naturally occurring changes to identify single objects. In the first step, we create pairs of images from different recordings and generate object candidates using optical flow and an off-the-shelf region proposal algorithm. Then, we use a self-supervised representation learning framework and cluster the extracted objects. We evaluate the performance of several existing clustering methods in an over-clustering setting, since the number of object classes is unknown in an unsupervised setup. Our experimental validation on a real-world dataset shows that the proposed system can successfully recognize objects and pre-annotate a dataset by exploiting a recapturing process.

关键词： Representation learning Clustering methods Semantics Clustering algorithms Self-supervised learning Indoor environment Recording

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Interactive RGB Image Segmentation via Depth-modified Click Encoding and Estimated Depth

Interactive RGB Image Segmentation via Depth-modified Click ...

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IEEE International Symposium on Multimedia

作者： Furkan Kaynar Adrian Michl Eckehard Steinbach Information and Technology Department of Computer Engineering Chair of Media Technology Technical University of Munich School of Computation Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany

ISBN: (纸本)9781665471732

Interactive image segmentation separates the object of interest in the scene from the background, using the help of human annotations. Click-based interactive segmentation methods typically receive positive and negative clicks from the user indicating the object of interest and the background. These clicks are encoded into click-maps to be processed further by a neural network. Although the depth information is known to improve the image segmentation accuracy, the previous work encodes only the locations of the clicks into the click-maps. We propose two novel click-map generation methods that modify the conventional click-maps using relative depth information. This depth information is estimated from the monocular RGB image. After retraining the baseline interactive segmentation method with our novel click-maps, the segmentation accuracy improved without requiring any additional input or increasing the network size. Experimental evaluations showed that our method yields a better mean segmentation accuracy on the Berkeley and DAVIS datasets than the baseline using conventional click-maps, and a comparable performance on the GrabCut dataset.

关键词： Training Image segmentation Image coding Annotations Neural networks Complexity theory

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