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The voraus-AD Dataset for Anomaly Detection in Robot Applications

IEEE TRANSACTIONS ON robotics 2024年 40卷 438-451页

作者： Brockmann, Jan Thies Rudolph, Marco Rosenhahn, Bodo Wandt, Bastian Voraus Robot GmbH D-30167 Hannover Germany Leibniz Univ Hannover Inst Informat Proc L3S D-30167 Hannover Germany Linkoping Univ Comp Vis Lab Linkoping Sweden

During the operation of industrial robots, unusual events may endanger the safety of humans and the quality of production. When collecting data to detect such cases, it is not ensured that data from all potentially occurring errors is included as unforeseeable events may happen over time. Therefore, anomaly detection (AD) delivers a practical solution, using only normal data to learn to detect unusual events. We introduce a dataset that allows training and benchmarking of anomaly detection methods for robotic applications based on machine data which will be made publicly available to the research community. As a typical robot task the dataset includes a pick-and-place application which involves movement, actions of the end effector, and interactions with the objects of the environment. Since several of the contained anomalies are not task-specific but general, evaluations on our dataset are transferable to other robotics applications as well. In addition, we present multivariate time-series flow (MVT-Flow) as a new baseline method for anomaly detection: It relies on deep-learning-based density estimation with normalizing flows, tailored to the data domain by taking its structure into account for the architecture. Our evaluation shows that MVT-Flow outperforms baselines from previous work by a large margin of 6.2% in area under receiving operator characteristic.

关键词： Dataset for anomaly detection (AD) deep learning in robotics and automation failure detection and recovery probability and statistical models

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Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation From Unlabeled Data

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IEEE TRANSACTIONS ON robotics 2024年 40卷 3146-3165页

作者： Williams, David S. W. Martini, Daniele De Gadd, Matthew Newman, Paul Univ Oxford Oxford Robot Inst Dept Engn Sci Oxford OX1 2JD England

Knowing when a trained segmentation model is encountering data that is different to its training data is important. Understanding and mitigating the effects of this play an important part in their application from a performance and assurance perspective-this being a safety concern in applications such as autonomous vehicles. This article presents a segmentation network that can detect errors caused by challenging test domains without any additional annotation in a single forward pass. As annotation costs limit the diversity of labeled datasets, we use easy-to-obtain, uncurated and unlabeled data to learn to perform uncertainty estimation by selectively enforcing consistency over data augmentation. To this end, a novel segmentation benchmark based on the sense-assess-eXplain (SAX) is used, which includes labeled test data spanning three autonomous-driving domains, ranging in appearance from dense urban to off-road. The proposed method, named gamma-SSL, consistently outperforms uncertainty estimation and out-of-distribution techniques on this difficult benchmark-by up to 10.7% in area under the receiver operating characteristic curve and 19.2% in area under the precision-recall curve in the most challenging of the three scenarios.

关键词： Autonomous vehicle (AV) navigation deep learning in robotics and automation introspection out-of-distribution (OoD) detection performance assessment semantic scene understanding uncertainty estimation

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Semi-Supervised Gait Generation With Two Microfluidic Soft Sensors

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IEEE robotics AND automation LETTERS 2019年第3期4卷 2501-2507页

作者： Kim, Dooyoung Kim, Min Kwon, Junghan Park, Yong-Lae Jo, Sungho Korea Adv Inst Sci & Technol Sch Comp Daejeon 305701 South Korea Seoul Natl Univ Dept Mech Engn & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Inst Adv Machines & Design Seoul 08826 South Korea

Nowadays, the use of deep learning for the calibration of soft wearable sensors has addressed the typical drawbacks of the microfluidic soft sensors, such as hysteresis and nonlinearity. However, previous studies have not yet resolved some of the design constraints such as the sensors are needed to he attached to the joints and many sensors are needed to track the human motion. Moreover, the previous methods also demand an excessive amount of data for sensor calibration which make the system impractical. In this letter, we present a gait motion generating method using only two microfluidic sensors. We select appropriate sensor positions with consideration of the deformation patterns of the lower-limb skins and mutual interference with soft actuators. Moreover, a semi-supervised deep learning model is proposed to reduce the size of calibration data. We evaluated the performance of the proposed model with various walking speeds. From the experiment, the proposed method showed a higher performance with smaller calibration dataset comparing to the other methods that are based on the supervised deep learning.

关键词： Wearable robots soft robot applications deep learning in robotics and automation

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learning Context Flexible Attention Model for Long-Term Visual Place Recognition

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IEEE robotics AND automation LETTERS 2018年第4期3卷 4015-4022页

作者： Chen, Zetao Liu, Lingqiao Sa, Inkyu Ge, Zongyuan Chli, Margarita Swiss Fed Inst Technol Vis Robot Lab CH-8092 Zurich Switzerland Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland Monash Univ ERes Ctr Melbourne Vic 3800 Australia

Identifying regions of interest in an image has long been of great importance in a wide range of tasks, including place recognition. In this letter, we propose a novel attention mechanism with flexible context, which can be incorporated into existing feed-forward network architecture to learn image representations for long-term place recognition. In particular, in order to focus on regions that contribute positively to place recognition, we introduce a multiscale context-flexible network to estimate the importance of each spatial region in the feature map. Our model is trained end-to-end for place recognition and can detect regions of interest of arbitrary shape. Extensive experiments have been conducted to verify the effectiveness of our approach and the results demonstrate that our model can achieve consistently better performance than the state of the art on standard benchmark datasets. Finally, we visualize the learned attention maps to generate insights into what attention the network has learned.

关键词： Localization deep learning in robotics and automation visual-based navigation

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DPC-Net: deep Pose Correction for Visual Localization

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IEEE robotics AND automation LETTERS 2018年第3期3卷 2424-2431页

作者： Peretroukhin, Valentin Kelly, Jonathan Univ Toronto Inst Aerosp Studies Space & Terr Autonomous Robot Syst Lab N York ON M3H 5T6 Canada

We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. In contrast to other methods that completely replace a classical visual estimator with a deep network, we propose an approach that uses a convolutional neural network to learn difficult-to-model corrections to the estimator from ground-truth training data. To this end, we derive a novel loss function for learning SE(3) corrections based on a matrix Lie groups approach, with a natural formulation for balancing translation and rotation errors. We use this loss to train a deep pose correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment. Using the KITTI odometry dataset, we demonstrate significant improvements to the accuracy of a computationally-efficient sparse stereo visual odometry pipeline, that render it as accurate as a modern computationally-intensive dense estimator. Further, we show how DPC-Net can be used to mitigate the effect of poorly calibrated lens distortion parameters.

关键词： deep learning in robotics and automation localization

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learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1053-1060页

作者： Banzhaf, Holger Sanzenbacher, Paul Baumann, Ulrich Zoellner, J. Marius Robert Bosch GmbH Corp Res Automated Driving D-71272 Renningen Germany FZI Res Ctr Informat Technol D-76131 Karlsruhe Germany

Sampling-based motion planning is an effective tool to compute safe trajectories for automated vehicles in complex environments. However, a fast convergence to the optimal solution can only be ensured with the use of problem-specific sampling distributions. Due to the large variety of driving situations within the context of automated driving, it is very challenging to manually design such distributions. This letter introduces, therefore, a data-driven approach utilizing a deep convolutional neural network (CNN): Given the current driving situation, future ego-vehicle poses can be directly generated from the output of the CNN allowing to guide the motion planner efficiently toward the optimal solution. A benchmark highlights that the CNN predicts future vehicle poses with a higher accuracy compared to uniform sampling and a state-of-the-art A*-based approach. Combining this CNN-guided sampling with the motion planner bidirectional RRT* reduces the computation time by up to an order of magnitude and yields a faster convergence to a lower cost as well as a success rate of 100% in the tested scenarios.

关键词： deep learning in robotics and automation non-holonomic motion planning motion and path planning

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learning a Low-Dimensional Representation of a Safe Region for Safe Reinforcement learning on Dynamical Systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2023年第5期34卷 2513-2527页

作者： Zhou, Zhehua Oguz, Ozgur S. Leibold, Marion Buss, Martin Tech Univ Munich Chair Automat Control Engn D-80290 Munich Germany Univ Stuttgart Max Planck Inst Intelligent Syst D-70569 Stuttgart Germany

For the safe application of reinforcement learning algorithms to high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning (SRL) framework. Based on the simplified system model, a low-dimensional representation of the safe region is identified and used to provide safety estimates for learning algorithms. However, finding a satisfying simplified system model for complex dynamical systems usually requires a considerable amount of effort. To overcome this limitation, we propose a general data-driven approach that is able to efficiently learn a low-dimensional representation of the safe region. By employing an online adaptation method, the low-dimensional representation is updated using the feedback data to obtain more accurate safety estimates. The performance of the proposed approach for identifying the low-dimensional representation of the safe region is illustrated using the example of a quadcopter. The results demonstrate a more reliable and representative low-dimensional representation of the safe region compared with previous works, which extends the applicability of the SRL framework.

关键词： Safety Computational modeling Heuristic algorithms Reinforcement learning Control systems Probabilistic logic Nonlinear dynamical systems Data-driven model order reduction deep learning in robotics and automation learning and adaptive systems safe reinforcement learning (SRL)

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Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3642-3649页

作者： Gangapurwala, Siddhant Mitchell, Alexander Havoutis, Ioannis Univ Oxford Oxford Robot Inst Dynam Robots Syst Grp Oxford OX1 2JD England

deep reinforcement learning (RL) uses model-free techniques to optimize task-specific control policies. Despite having emerged as a promising approach for complex problems, RL is still hard to use reliably for real-world applications. Apart from challenges such as precise reward function tuning, inaccurate sensing and actuation, and non-deterministic response, existing RL methods do not guarantee behavior within required safety constraints that are crucial for real robot scenarios. In this regard, we introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained proximal policy optimization (CPPO) for tracking base velocity commands while following the defined constraints. We introduce schemes which encourage state recovery into constrained regions in case of constraint violations. We present experimental results of our training method and test it on the real ANYmal quadruped robot. We compare our approach against the unconstrained RL method and show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.

关键词： Optimization Training Task analysis Legged locomotion Mathematical model Tuning deep learning in robotics and automation AI-based methods legged robots robust adaptive control of robotic systems underactuated robots

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Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1792-1799页

作者： Zhou, Xiao-Yun Yang, Guang-Zhong Imperial Coll London Hamlyn Ctr Robot Surg London SW7 2AZ England

Two-dimensional (2-D) biomedical semantic segmentation is important for robotic vision in surgery. Segmentation methods based on deep convolutional neural network (DCNN) can out-perform conventional methods in terms of both accuracy and levels of automation. One common issue in training a DCNN for biomedical semantic segmentation is the internal covariate shift where the training of convolutional kernels is encumbered by the distribution change of input features, hence both the training speed and performance are decreased. Batch normalization (BN) is the first proposed method for addressing internal covariate shift and is widely used. Instance normalization (IN) and layer normalization (LN) have also been proposed. Group normalization (GN) is proposed more recently and has not yet been applied to 2-D biomedical semantic segmentation (GN was used in 3-D biomedical semantic segmentation in [P.-Y. Kao, T. Ngo, A. Zhang, J. Chen, and B. Manjunath, Brain tumor segmentation and tractographic feature extraction from structural MR images for overall survival prediction 2018, arXiv:1807.07716], however, no specific validations on GN were given). Most DCNNs for biomedical semantic segmentation adopt BN as the normalization method by default, without reviewing its performance. In this letter, four normalization methods-BN, IN, LN, and GN are compared in details, specifically for 2-D biomedical semantic segmentation. U-Net is adopted as the basic DCNN structure. Three datasets regarding the right ventricle, aorta, and left ventricle are used for the validation. The results show that detailed subdivision of the feature map, i.e., GN with a large group number or IN, achieves higher accuracy. This accuracy improvement mainly comes from better model generalization. Codes are uploaded and maintained at Xiao-Yun Zhou's Github.

关键词： Computer vision formedical robotics deep learning in robotics and automation surgical robotics: planning visualbased navigation

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SGANVO: Unsupervised deep Visual Odometry and Depth Estimation With Stacked Generative Adversarial Networks

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IEEE robotics AND automation LETTERS 2019年第4期4卷 4431-4437页

作者： Feng, Tuo Gu, Dongbing Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

Recently end-to-end unsupervised deep learning methods have demonstrated an impressive performance for visual depth and ego-motion estimation tasks. These data-based learning methods do not rely on the same limiting assumptions that geometry-based methods do. The encoder-decoder network has been widely used in the depth estimation and the RCNN has brought significant improvements in the ego-motion estimation. Furthermore, the latest use of generative adversarial nets (GANs) in depth and ego-motion estimation has demonstrated that the estimation could be further improved by generating pictures in the game learning process. This paper proposes a novel unsupervised network system for visual depth and ego-motion estimation-stacked generative adversarial network. It consists of a stack of GAN layers, of which the lowest layer estimates the depth and ego-motion while the higher layers estimate the spatial features. It can also capture the temporal dynamic due to the use of a recurrent representation across the layers. We select the most commonly used KITTI data set for evaluation. The evaluation results show that our proposed method can produce better or comparable results in depth and ego-motion estimation.

关键词： Estimation Generators Image reconstruction Generative adversarial networks Visual odometry Training Gallium nitride deep learning in robotics and automation SLAM Localization Mapping

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