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检索条件"机构=Institute of Computer Science and Robotics"

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On Kinetic Modeling of the Process of Destruction of Solid Paraffin

On Kinetic Modeling of the Process of Destruction of Solid P...

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： A. A. Kornilova S. I. Mustafina D. A. Kornilov E. R. Gizzatova A.G. Mustafin R. N. Galiakhmetov Institute of Mathematics Computer Science and Robotics Ufa University of Science and Technology Ufa Russia Institute of Chemistry and Protection in Emergency Situations Ufa University of Science and Technology Ufa Russia Institute of Technologies and Materials Ufa University of Science and Technology Ufa Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The work revealed that the destruction process occurs in three stages. Each stage is characterized by its own kinetic model. The values of the pre-exponential factor and the activation energy of the reaction rate constant were obtained. The calculation according to this method with the found values of the reaction rate constants showed a fairly good match with the experimental data.

关键词： Solid modeling Automation Solids Control systems Mathematical models Energy efficiency Kinetic theory

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StereoNavNet: Learning to Navigate using Stereo Cameras with Auxiliary Occupancy Voxels

StereoNavNet: Learning to Navigate using Stereo Cameras with...

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

作者： Hongyu Li Taşkın Padır Huaizu Jiang Department of Computer Science Brown University Providence RI Institute for Experiential Robotics Northeastern University Boston MA Khoury College of Computer Sciences Northeastern University Boston MA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Visual navigation has received significant attention recently. Most of the prior works focus on predicting navigation actions based on semantic features extracted from visual encoders. However, these approaches often rely on large datasets and exhibit limited generalizability. In contrast, our approach draws inspiration from traditional navigation planners that operate on geometric representations, such as occupancy maps. We propose StereoNavNet (SNN), a novel visual navigation approach employing a modular learning framework comprising perception and policy modules. Within the perception module, we estimate an auxiliary 3D voxel occupancy grid from stereo RGB images and extract geometric features from it. These features, along with user-defined goals, are utilized by the policy module to predict navigation actions. Through extensive empirical evaluation, we demonstrate that SNN outperforms baseline approaches in terms of success rates, success weighted by path length, and navigation error. Furthermore, SNN exhibits better generalizability, characterized by maintaining leading performance when navigating across previously unseen environments.

关键词： Measurement Visualization Three-dimensional displays Navigation Semantics Feature extraction Cameras Planning Long short term memory Testing

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Nested Deep Feature Attention Module for Underwater Image Enhancement 3

Nested Deep Feature Attention Module for Underwater Image En...

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3rd International conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Xiao, Yeqing Wang, Qiang Li, Yupeng Tang, Yandong School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China Key Laboratory of Manufacturing Industrial Integrated Shenyang University Shenyang110044 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

The captured underwater images always suffer degradations because of absorption and light scattering in water. Thus, underwater image enhancement becomes indispensable as a precondition to carry out underwater tasks. Thus, we put forward an end-to-end structure to solve the problem of underwater image degradation. Our network uses continuously stacked deep-layer feature extraction modules to exploit more significant features. For purpose of enhancing the image contrast, we recover high-frequency information by hiring the nested residual attention groups in feature extraction modules, which can adaptively extract more deeper features. Moreover, channel attention module is hired in residual groups to emphasize important information. Comparing with other state-of-the-art approaches, our network performs best on quantitative and qualitative evaluations at datasets EUVP and UIEBD. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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AirShot: Efficient Few-Shot Detection for Autonomous Exploration

AirShot: Efficient Few-Shot Detection for Autonomous Explora...

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

作者： Zihan Wang Bowen Li Chen Wang Sebastian Scherer Institute for Imaging Data and Communications School of Engineering The University of Edinburgh UK AirLab Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Computer Science and Engineering Spatial AI & Robotics (SAIR) Lab Institute for Artificial Intelligence and Data Science University at Buffalo NY USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Few-shot object detection has drawn increasing attention in the field of robotic exploration, where robots are required to find unseen objects with a few online provided examples. Despite recent efforts have been made to yield online processing capabilities, slow inference speeds of low-powered robots fail to meet the demands of real-time detection-making them impractical for autonomous exploration. Existing methods still face performance and efficiency challenges, mainly due to unreliable features and exhaustive class loops. In this work, we propose a new paradigm AirShot, and discover that, by fully exploiting the valuable correlation map, AirShot can result in a more robust and faster few-shot object detection system, which is more applicable to robotics community. The core module Top Prediction Filter (TPF) can operate on multi-scale correlation maps in both the training and inference stages. During training, TPF supervises the generation of a more representative correlation map, while during inference, it reduces looping iterations by selecting top-ranked classes, thus cutting down on computational costs with better performance. Surprisingly, this dual functionality exhibits general effectiveness and efficiency on various off-the-shelf models. Exhaustive experiments on COCO2017, VOC2014, and SubT datasets demonstrate that TPF can significantly boost the efficacy and efficiency of most off-the-shelf models, achieving up to 36.4% precision improvements along with 56.3% faster inference speed. Code and Data are at: https://***/ImNotPrepared/AirShot.

关键词： Training Correlation Codes Computational modeling Atmospheric modeling Object detection Real-time systems Computational efficiency Intelligent robots Faces

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VARIATIONAL AUTOENCODERS IN THE PRESENCE OF LOW-DIMENSIONAL DATA: LANDSCAPE AND IMPLICIT BIAS 10

VARIATIONAL AUTOENCODERS IN THE PRESENCE OF LOW-DIMENSIONAL ...

引用

10th International Conference on Learning Representations, ICLR 2022

作者： Koehler, Frederic Mehta, Viraj Zhou, Chenghui Risteski, Andrej Department of Computer Science Stanford University United States Robotics Institute Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States

Variational Autoencoders (VAEs) are one of the most commonly used generative models, particularly for image data. A prominent difficulty in training VAEs is data that is supported on a lower dimensional manifold. Recent work by Dai and Wipf (2020) proposes a two-stage training algorithm for VAEs, based on a conjecture that in standard VAE training the generator will converge to a solution with 0 variance which is correctly supported on the ground truth manifold. They gave partial support for this conjecture by showing that some optima of the VAE loss do satisfy this property, but did not analyze the training dynamics. In this paper, we show that for linear encoders/decoders, the conjecture is true-that is the VAE training does recover a generator with support equal to the ground truth manifold-and does so due to an implicit bias of gradient descent rather than merely the VAE loss itself. In the nonlinear case, we show that VAE training frequently learns a higher-dimensional manifold which is a superset of the ground truth manifold. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Gradient methods

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Effective Carbon Tax Learning via Cap and Trade 5

Effective Carbon Tax Learning via Cap and Trade

引用

5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Liang, Jinhao Jiang, Wenqian Wu, Chenye Xidian University School of Computer Science and Technology Shaanxi Xi'an China The Chinese University of Hong Kong Shenzhen and Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering Guangdong Shenzhen China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

Global warming warrants the global adoption of carbon reduction schemes, among which the two most popular ones are carbon tax (CT) and the cap-and-trade (CAT) program with quite different implementations. CT levies taxes on the energy generation while CAT directly limits the total carbon emission in the system. Hence, in practice, CAT is more adjustable than CT due to the long legislation process for altering tax rate, though CT is much easier to implement. In this paper, we seek to learn the most effective tax rate through CAT programs by establishing the equivalence between CT and CAT. Through bridging the two schemes, we use numerical study to highlight the different dynamics for the schemes to converge to the equivalent point. © 2022 IEEE.

关键词： Taxation

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Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments

Greedy Perspectives: Multi-Drone View Planning for Collabora...

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

作者： Krishna Suresh Aditya Rauniyar Micah Corah Sebastian Scherer Olin College of Engineering Needham MA USA Robotics Institute School of Computer Science at Carnegie Mellon University Pittsburgh PA USA Department of Computer Science Colorado School of Mines Golden CO USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Deployment of teams of aerial robots could enable large-scale filming of dynamic groups of people (actors) in complex environments for applications in areas such as team sports and cinematography. Toward this end, methods for submodular maximization via sequential greedy planning can enable scalable optimization of camera views across teams of robots but face challenges with efficient coordination in cluttered environments. Obstacles can produce occlusions and increase chances of inter-robot collision which can violate requirements for near-optimality guarantees. To coordinate teams of aerial robots in filming groups of people in dense environments, a more general view-planning approach is required. We explore how collision and occlusion impact performance in filming applications through the development of a multi-robot multi-actor view planner with an occlusion-aware objective for filming groups of people and compare with a formation planner and a greedy planner that ignores inter-robot collisions. We evaluate our approach based on five test environments and complex multi-actor behaviors. Compared with a formation planner, our sequential planner generates 14% greater view reward for filming the actors in three scenarios and comparable performance to formation planning on two others. We also observe near identical view rewards for sequential planning both with and without inter-robot collision constraints which indicates that robots are able to avoid collisions without impairing performance in the perception task. Overall, we demonstrate effective coordination of teams of aerial robots in environments cluttered with obstacles that may cause collisions or occlusions and for filming groups that may split, merge, or spread apart. Our implementation and the data used to produce results for this paper are available via the companion website: https://***/

关键词： Robot kinematics Robot vision systems Collaboration Autonomous aerial vehicles Planning Collision avoidance Optimization Intelligent robots Faces Sports

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SEQUEL: Semi-Supervised Preference-based RL with Query Synthesis via Latent Interpolation

SEQUEL: Semi-Supervised Preference-based RL with Query Synth...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel Marta Simon Holk Christian Pek Iolanda Leite Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures Dept. of Cognitive Robotics TU Delft Delft The Netherlands

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Preference-based reinforcement learning (RL) poses as a recent research direction in robot learning, by allowing humans to teach robots through preferences on pairs of desired behaviours. Nonetheless, to obtain realistic robot policies, an arbitrarily large number of queries is required to be answered by humans. In this work, we approach the sample-efficiency challenge by presenting a technique which synthesizes queries, in a semi-supervised learning perspective. To achieve this, we leverage latent variational autoencoder (VAE) representations of trajectory segments (sequences of state-action pairs). Our approach manages to produce queries which are closely aligned with those labeled by humans, while avoiding excessive uncertainty according to the human preference predictions as determined by reward estimations. Additionally, by introducing variation without deviating from the original human’s intents, more robust reward function representations are achieved. We compare our approach to recent state-of-the-art preference-based RL semi-supervised learning techniques. Our experimental findings reveal that we can enhance the generalization of the estimated reward function without requiring additional human intervention. Lastly, to confirm the practical applicability of our approach, we conduct experiments involving actual human users in a simulated social navigation setting. Videos of the experiments can be found at https://***/view/rl-sequel

关键词： Interpolation Uncertainty Navigation Estimation Reinforcement learning Semisupervised learning Robot learning

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Semantic-guided Image Feature Matching for Agricultural Robots 4

Semantic-guided Image Feature Matching for Agricultural Robo...

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4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Yu, Zhenghong Zhu, Xiuli Zhou, Huabing College of Robotics Guangdong Polytechnic of Science and Technology China School of Computer Science and Engineering Wuhan Institute of Technology China

ISBN: (纸本)9781450384087

Image feature matching is one of the most important basic techniques for agricultural robots. This task has been tried, but because of the complexity of the image, especially the non-rigid transformations, the performance is still far from satisfactory. This paper proposed a semantic-guided feature matching method for both agricultural mobile robot and UAV via semantic segmentation and probabilistic inference. Given an original image pair, a learning-based method, which is very active to the challenging variation of outdoor luminosity, is introduced firstly to produce a sematic image pair. This step shifts the robot's focus to the task itself, while improving matching efficiency at the same time. Then, based on the characteristic similarity of the ROI region of the semantic image pair, a series of putative correspondences are generated. Next, the focuses on eliminating outliers from that series while deducting the transformation, where this procedure was developed on a maximum likelihood Bayesian model. The paper has also applied three effective regularization techniques to the correspondence, which helped to come up with the best solution. Finally, as the problem was solved by EM algorithm and extensive experiments on those images taken from the real farmland, it is safe to say that the accurate results of this method are superior to other state-of-the-art methods. © 2021 ACM.

关键词： Mobile robots

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A Novel Low Light Image Enhancement Method Based on Multi-Attention Generative Adversarial Networks 10

A Novel Low Light Image Enhancement Method Based on Multi-At...

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10th International Conference on Information Systems and Computing Technology, ISCTech 2022

作者： Zhang, Saijun Hou, Xu School of Computer Science and Engineering Northeastern University Shenyang China Institute of Artificial Intelligence and Marine Robotics College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (纸本)9798350332933

Images taken in low light realistic conditions cannot capture enough illumination to show more details, so that the recovered images show color deviation and unclear edge textures along with much noise information in the experiments of low light image enhancement. In order to obtain high brightness and clear images, this paper designs a network based on multi-attention Generative Adversarial Networks for low light image enhancement. The network uses a low illumination enhancement module that incorporates the non-local illumination attention and edge aware attention proposed in this paper. Non-local illumination attention can combine global information to quickly capture the relationship of long-range features, solving the color deviation problem while reducing the number of convolution layers. Edge aware attention focuses on local information at the edges, highlighting the edge contours of the image while smoothing out the noise. These two attentions serve as a complement to each other. And Attentional Feature Fusion is used to fuse the features of different branches and retain more valid information. It is proved that our proposed low light enhancement network is superior to other networks in terms of color fidelity, edge clarity, and denoising. © 2022 IEEE.

关键词： Image enhancement

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