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Real-Time 3-D Shape Instantiation for Partially Deployed Stent Segments From a Single 2-D Fluoroscopic Image in Fenestrated Endovascular Aortic Repair

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3703-3710页

作者： Zheng, Jian-Qing Zhou, Xiao-Yun Riga, Celia Yang, Guang-Zhong Imperial Coll London Hamlyn Ctr Robot Surg London SW7 2AZ England St Marys Hosp Reg Vasc Unit London W2 1NY England Imperial Coll London Acad Div Surg London SW7 2A2 England

In fenestrated endovascular aortic repair (FEVAR), accurate alignment of stent graft fenestrations or scallops with aortic branches is essential for establishing complete blood flow perfusion. Current navigation is largely based on two-dimensional (2-D) fluoroscopic images, which lacks 3-D anatomical information, thus causing a longer operation time and high risks of radiation exposure. Previously, 3-D shape instantiation frameworks for realtime 3-D shape reconstruction of fully deployed or fully compressed stent grafts from a single 2-D fluoroscopic image have been proposed for 3-D navigation in FEVAR. However, thesemethods could not instantiate partially deployed stent segments, as the 3-D marker references are unknown. In this letter, an adapted graph convolutional network (GCN) is proposed to predict 3-D partially deployed marker references from 3-D fully deployed marker references. As the original GCN is for classification, in this letter, the coarsening layers are removed and the softmax function at the network end is replaced with linear mapping for regression. The derived 3-D marker references and the 2-D marker positions are used to instantiate the partially deployed stent segment, combined with the previous 3-D shape instantiation framework. Validations were performed on three typical stent grafts and five patient-specific 3-D printed aortic aneurysm phantoms. Reasonable performances with average mesh distance errors from 1.0 to 2.4 mm and average angular errors around 7.2. were achieved.

关键词： deep learning in robotics and automation computer vision for medical robotics surgical robotics: planning visual-based navigation motion and path planning

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Enabling Visual Action Planning for Object Manipulation Through Latent Space Roadmap

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IEEE TRANSACTIONS ON robotics 2023年第1期39卷 57-75页

作者： Lippi, Martina Poklukar, Petra Welle, Michael C. Varava, Anastasia Yin, Hang Marino, Alessandro Kragic, Danica KTH Royal Inst Technol S-11428 Stockholm Sweden KTH Royal Inst Technol EECS RPL S-11428 Stockholm Sweden KTH Royal Inst Technol Div Robot Percept & Learning S-11428 Stockholm Sweden KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-11428 Stockholm Sweden Roma Tre Univ I-00154 Rome Italy Univ Cassino & Southern Lazio I-03043 Cassino Italy

In this article, we present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces, focusing on manipulation of deformable objects. We propose a latent space roadmap (LSR) for task planning, which is a graph-based structure globally capturing the system dynamics in a low-dimensional latent space. Our framework consists of the following three parts. First, a mapping module (MM) that maps observations is given in the form of images into a structured latent space extracting the respective states as well as generates observations from the latent states. Second, the LSR, which builds and connects clusters containing similar states in order to find the latent plans between start and goal states, extracted by MM. Third, the action proposal module that complements the latent plan found by the LSR with the corresponding actions. We present a thorough investigation of our framework on simulated box stacking and rope/box manipulation tasks, and a folding task executed on a real robot.

关键词： Task analysis Planning Visualization Robots Trajectory Stacking Heuristic algorithms deep learning in robotics and automation Latent Space Planning Manipulation Planning Visual learning

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A deep learning Approach for Probabilistic Security in Multi-Robot Teams

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IEEE robotics AND automation LETTERS 2019年第4期4卷 4262-4269页

作者： Wehbe, Remy Williams, Ryan K. Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Blacksburg VA 24061 USA

In this letter, we train a convolutional neural network (CNN) to predict the probability of security of a multi-robot system (MRS) when robot interactions are probabilistic. In the context of MRSs, probabilistic security is defined using the control-theoretic notion of left invertibility, a necessary and sufficient condition to avoid perfect attacks. As the probabilistic security problem is NP-Complete, current solutions fail to generalize as the size of the MRS increases. Fortunately, deep neural networks have shown promising results in the efficient computation of solutions to hard problems, which motivates our CNN-based approach. In this context, formulating a method for data generation is non-trivial due to the large space of available interaction graph topologies and training biases introduced by random sampling. As such, we use a two-step approach for data generation where we first explore the space of available topologies, then populate the sampled topologies with probability distributions, all while preventing any biases from occurring in the data. We then train a CNN with convolution layers specifically tailored for graph adjacency matrices. Finally, the validity of our results is demonstrated through simulations.

关键词： Multi-Robot Systems deep learning in robotics and automation Distributed Robot Systems Networked Robots

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learning Affordance Segmentation for Real-World Robotic Manipulation via Synthetic Images

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1140-1147页

作者： Chu, Fu-Jen Xu, Ruinian Vela, Patricio A. Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA

This letter presents a deep learning framework to predict the affordances of object parts for robotic manipulation. The framework segments affordance maps by jointly detecting and localizing candidate regions within an image. Rather than requiring annotated real-world images, the framework learns from synthetic data and adapts to real-world data without supervision. Themethod learns domain-invariant region proposal networks and task-level domain adaptation components with regularization on the predicted domains. A synthetic version of the UMD data set is collected for autogenerating annotated, synthetic input data. Experimental results show that the proposed method outperforms an unsupervised baseline, and achieves performance close to state-of-the-art supervised approaches. An ablation study establishes the performance gap between the proposedmethod and the supervised equivalent (30%). Real-world manipulation experiments demonstrate use of the affordance segmentations for task execution, which achieves the same performance with supervised approaches.

关键词： Perception for Grasping and Manipulation deep learning in robotics and automation RGB-D Perception

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learning Object Grasping for Soft Robot Hands

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IEEE robotics AND automation LETTERS 2018年第3期3卷 2370-2377页

作者： Choi, Changhyun Schwarting, Wilko DelPreto, Joseph Rus, Daniela MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

We present a three-dimensional deep convolutional neural network (3D CNN) approach for grasping unknown objects with soft hands. Soft hands are compliant and capable of handling uncertainty in sensing and actuation, but come at the cost of unpredictable deformation of the soft fingers. Traditional model-driven grasping approaches, which assume known models for objects, robot hands, and stable grasps with expected contacts, are inapplicable to such soft hands, since predicting contact points between objects and soft hands is not straightforward. Our solution adopts a deep CNN approach to find good caging grasps for previously unseen objects by learning effective features and a classifier from point cloud data. Unlike recent CNN models applied to robotic grasping which have been trained on 2D or 2.5D images and limited to a fixed top grasping direction, we exploit the power of a 3D CNN model to estimate suitable grasp poses from multiple grasping directions (top and side directions) and wrist orientations, which has great potential for geometry-related robotic tasks. Our soft hands guided by the 3D CNN algorithm show 87% successful grasping on previously unseen objects. A set of comparative evaluations shows the robustness of our approach with respect to noise and occlusions.

关键词： Perception for grasping and manipulation deep learning in robotics and automation

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Paired Recurrent Autoencoders for Bidirectional Translation Between Robot Actions and Linguistic Descriptions

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3441-3448页

作者： Yamada, Tatsuro Matsunaga, Hiroyuki Ogata, Tetsuya Waseda Univ Dept Intermedia Art & Sci Tokyo 1698555 Japan

We propose a novel deep learning framework for bidirectional translation between robot actions and their linguistic descriptions. Our model consists of two recurrent autoencoders (RAEs). One RAE learns to encode action sequences as fixed-dimensional vectors in a way that allows the sequences to be reproduced from the vectors by its decoder. The other RAE learns to encode descriptions in a similar way. In the learning process, in addition to reproduction losses, we create another loss function whereby the representations of an action and its corresponding description approach each other in the latent vector space. Across the shared representation, the trained model can produce a linguistic description given a robot action. The model is also able to generate an appropriate action by receiving a linguistic instruction, conditioned on the current visual input. Visualization of the latent representations shows that the robot actions are embedded in a semantically compositional way in the vector space by being learned jointly with descriptions.

关键词： deep learning in robotics and automation Al-based methods neurorobotics

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Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds

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IEEE TRANSACTIONS ON robotics 2024年 40卷 2357-2372页

作者： Zeller, Matthias Sandhu, Vardeep S. Mersch, Benedikt Behley, Jens Heidingsfeld, Michael Stachniss, Cyrill CARIAD SE D-53115 Bonn Germany Univ Bonn D-53115 Bonn Germany CARIAD SE D-71297 Monsheim Germany Univ Oxford Dept Engn Sci Oxford OX1 2JD England Lamarr Inst Machine Learning & Artificial Intellig Dortmund Germany

The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion information and face limitations under adverse weather. Radar sensors overcome these limitations and provide Doppler velocities, delivering direct information on dynamic objects. In this article, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks. Our radar instance transformer enriches the current radar scan with temporal information without passing aggregated scans through a neural network. We propose a full-resolution backbone to prevent information loss in sparse point cloud processing. Our instance transformer head incorporates essential information to enhance segmentation but also enables reliable, class-agnostic instance assignments. In sum, our approach shows superior performance on the new moving instance segmentation benchmarks, including diverse environments, and provides model-agnostic modules to enhance scene interpretation.

关键词： Radar Point cloud compression Feature extraction Transformers Task analysis Encoding Sensors deep learning in robotics and automation object detection radar perception segmentation and categorization semantic scene understanding

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Vision-Based Estimation of Driving Energy for Planetary Rovers Using deep learning and Terramechanics

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3876-3883页

作者： Higa, Shoya Iwashita, Yumi Otsu, Kyohei Ono, Masahiro Lamarre, Olivier Didier, Annie Hoffmann, Mark CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA Univ Toronto Inst Aerosp Studies STARS Lab Toronto ON M3H 5T6 Canada

This letter presents a prediction algorithm of driving energy for future Mars rover missions. The majority of future Mars rovers would be solar-powered, which would require energy-optimal driving to maximize the range with limited energy. The essential and arguably the most challenging technology for realizing energy-optimal driving is the capability to predict the driving energy, which is needed to construct an energy-aware cost function for path planning. In this letter, we propose vision-based algorithms to remotely predict the driving energy consumption using machine learning. Specifically, we develop and compare two machine-learning models in this letter, namely VeeGer-Energy Net and Veeger-TerramechanicsNet, respectively. The former is trained directly using recorded power, while the latter estimates terrain parameters from the images using a simplified-terramechanics model, and calculate the power based on the model. The two approaches are fully automated self-supervised learning algorithms. To combine RGB and depth images efficiently with high accuracy, we propose a new network architecture called Two-PNASNet-5, which is based on PNASNet-5. We collected a new dataset to verify the effectiveness of the proposed approaches. Comparison of the two approaches showed that Veeger-TerramechanicsNet had better performance than VeeGer-EnergyNet.

关键词： Space robotics and automation wheeled robotics deep learning in robotics and automation

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Real Time Trajectory Prediction Using deep Conditional Generative Models

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IEEE robotics AND automation LETTERS 2020年第2期5卷 970-976页

作者： Gomez-Gonzalez, Sebastian Prokudin, Sergey Schoelkopf, Bernhard Peters, Jan Max Planck Intelligent Syst D-72072 Tubingen Germany Tech Univ Darmstadt D-64289 Darmstadt Germany

Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot system to make decisions that trade off between reaction time and accuracy in the predictions. Despite the recent advances in deep learning, it is still challenging to make long term accurate predictions with the low latency required by real time robotic systems. In this letter, we propose a deep conditional generative model for trajectory prediction that is learned from a data set of collected trajectories. Our method uses encoder and decoder deep networks that map complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations. The encoder and decoder networks are trained using stochastic gradient variational Bayes. In the experiments, we show that our model provides more accurate long term predictions with a lower latency than popular models for trajectory forecasting like recurrent neural networks or physical models based on differential equations. Finally, we test our proposed approach in a robot table tennis scenario to evaluate the performance of the proposed method in a robotic task with hard real time constraints.

关键词： deep learning in robotics and automation probability and statistical methods

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Fully Automated Annotation With Noise-Masked Visual Markers for deep-learning-Based Object Detection

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1972-1977页

作者： Kiyokawa, Takuya Tomochika, Keita Takamatsu, Jun Ogasawara, Tsukasa Nara Inst Sci & Technol Div Informat Sci Nara 6300192 Japan

Automated factories use deep-learning-based vision systems to accurately detect various products. However, training such vision systems requires manual annotation of a significant amount of data to optimize the large number of parameters of the deep convolutional neural networks. Such manual annotation is very time-consuming and laborious. To reduce this burden, we propose a fully automated annotation approach without any manual intervention. To do this, we associate one visual marker with one object and capture them in the same image. However, if an image showing the marker is used for training, normally, the neural network learns the marker as a feature of the object. By hiding the marker with a noise mask, we succeeded in reducing this erroneous learning. Experiments verified the effectiveness of the proposed method in comparison with manual annotation, in terms of both the time needed to collect training data and the resulting detection accuracy of the vision system. The time required for data collection was reduced from 16.1 to 1.87 h. The accuracy of the vision system trained with the proposed method was 87.3%, which is higher than the accuracy of a vision system trained with the manual method.

关键词： Computer vision for automation deep learning in robotics and automation object detection segmentation and categorization

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