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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24967 条记录，以下是3911-3920 订阅

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Temporal Segment Fourier Transformer for Aciton Quality Assessment

Temporal Segment Fourier Transformer for Aciton Quality Asse...

引用

IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Jifeng Han Yanduo Zhang Pan Tang Tao Lu Jiaming Wang Fangqun Gao Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China State Key Laboratory of Power Grid Environmental Protection China Electric Power Research Institute Wuhan China Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

Action Quality Assessment (AQA) plays an important role in video analysis, and unlike action recognition tasks, AQA tasks are more challenging because they require attention to subtle differences within the class. In order to capture fine-grained temporal semantic information, we designed a temporal segmentation transformer (TST) using the powerful sequence modeling capability of transformer to parse the complete action sequence into sub-action parts. The AQA methods mainly based on transformer structure usually has high computational overhead and inference time, so we designed a Fourier-based token mixer (FTM) to replace the self-attention layer, which significantly reduces computational complexity while maintaining accuracy. The experimental results on two benchmark datasets show that our FTM method effectively accelerates training and inference without significantly affecting accuracy.

关键词： Training Accuracy Mechatronics Fourier transforms Computational modeling Semantics Transformers Quality assessment robotics and automation Mixers

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Toward Robust and Efficient Online Adaptation for Deep Stereo Depth Estimation

Toward Robust and Efficient Online Adaptation for Deep Stere...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Knowles, Milo Peretroukhin, Valentin Greene, W. Nicholas Roy, Nicholas MIT Comp Sci & Artificial Intelligence Lab CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728190778

Although deep neural networks have achieved state-of-the-art performance for stereo depth estimation, they can suffer from a significant drop in accuracy when tested on images from novel domains. Recent work has shown that self-supervised online adaptation is a promising approach for closing this performance gap. In this work, we address three unsolved challenges for online adaptation. First, we propose a method for detecting novel environments, allowing us to trigger adaptation and notify downstream systems that depth predictions are unreliable. We find that the feature similarity scores from our deep stereo network can be leveraged for out-of-distribution (OOD) detection, providing the necessary starting criterion for adaptation. Next, we use online validation to terminate adaptation when it stops improving performance, allowing us to free up computational resources. Finally, we demonstrate that existing methods for continuous adaptation cause catastrophic forgetting of the training domain. By augmenting adaptation with experience replay, we retain high accuracy in the training domain while rapidly improving performance in novel environments. In sum, these three contributions form the basis of a more robust and efficient deep stereo system that can recognize and adapt to new environments without forgetting.

关键词： Training Deep learning automation conferences Estimation Feature extraction

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An Upper Confidence Bound for Simultaneous Exploration and Exploitation in Heterogeneous Multi-Robot Systems

An Upper Confidence Bound for Simultaneous Exploration and E...

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IEEE international conference on robotics and automation (ICRA)

作者： Lee, Ki Myung Brian Kong, Felix Cannizzaro, Ricardo Palmer, Jennifer L. Johnson, David Yoo, Chanyeol Fitch, Robert Univ Technol Sydney Ultimo NSW 2006 Australia Dept Def Def Sci & Technol Grp Melbourne Vic Australia Mission Systoms Pty Ltd Sydney NSW Australia

ISBN: (纸本)9781728190778

Heterogeneous multi-robot systems are advantageous for operations in unknown environments because functionally specialised robots can gather environmental information, while others perform tasks. We define this decomposition as the scout-task robot architecture and show how it avoids the need to explicitly balance exploration and exploitation by permitting the system to do both simultaneously. The challenge is to guide exploration in a way that improves overall performance for time-limited tasks. We derive a novel upper confidence bound for simultaneous exploration and exploitation based on mutual information and present a general solution for scout-task coordination using decentralised Monte Carlo tree search. We evaluate the performance of our algorithms in a multi-drone surveillance scenario in which scout robots are equipped with low-resolution, long-range sensors and task robots capture detailed information using short-range sensors. The results address a new class of coordination problem for heterogeneous teams that has many practical applications.

关键词： Monte Carlo methods automation Robot kinematics Surveillance conferences Robot sensing systems Sensors

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Self-Supervised Multi-View Reconstruction of Eyeglasses Frames with Thin Structures 16

Self-Supervised Multi-View Reconstruction of Eyeglasses Fram...

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2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Li, Lili Zhou, Lang Zhang, Fan Deng, Pan Ji, Ziyue Su, Zhiyong College of Information Engineering Nanjing University of Finance and Economics China School of Automation Nanjing University of Science and Technology China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Using traditional depth and image-based methods for reconstructing eyeglass frames poses significant challenges due to their unique properties, such as minimal textural features and thin components. In this paper, we propose a novel self-supervised approach for reconstructing eyeglass frames based on vertex displacement prediction. This method enables the precise 3D shape recovery of full-frame eyeglass models from multi-view images. To begin, we first establish an eyeglasses frame template, leveraging prior and domain-specific knowledge. Subsequently, when provided with an input image of an eyeglass frame characterized by thin structures and few texture features, we develop an iterative self-supervised camera pose estimation module to accurately predict the camera pose. Following this, we employ a self-supervised vertex offset prediction network to extract features from both the input multi-view images and the 3D template eyeglass frame model. This network predicts the vertex displacement of the frame to be reconstructed relative to the template frame model. We iteratively optimize the network to obtain precise vertex offsets by minimizing the image loss between rendered images and input images using differentiable rendering. Experimental findings from both synthetic and real datasets demonstrate the effectiveness of our proposed method. © 2025 SPIE.

关键词： Self-supervised learning

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Enhanced Surveillance Abilities Through Object Detection in Autonomous Robots

Enhanced Surveillance Abilities Through Object Detection in ...

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IEEE international conference on System, Computation, automation and Networking (ICSCAN)

作者： J.V. Pesha Golvainbec Jotham Thirumalai C.K. Nithish Kumar. V Sasirekha. M Dept. of Robotics and Automation Manakula Vinayagar Institute of Technology Puducherry

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The system emphasizes the operation of an autonomous environment monitoring robot with the capability of object detection and tracking using OpenCV and mobile application, we gather real-time video data from the environment with the help of an ESP-32 cam module and cast the data to the respective mobile application forum for the visualization and analysis of the surrounding space with the help of WIFI (wireless fidelity). We use an ESP-32 camera-based autonomous surveillance robot integrated with a camera and ESP-32 microcontroller for real-time object detection using OpenCV with the help of pretrained models like mobile net, tensor flow, etc. The ESP-32 cam module processes images controls motors for movement and communicates wirelessly for security applications offering a versatile solution for tracking and monitoring operations.

关键词： Tensors Tracking Surveillance Object detection Streaming media Real-time systems Mobile applications Trajectory Security Wireless fidelity

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WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditions

WeatherDepth: Curriculum Contrastive Learning for Self-Super...

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IEEE international conference on robotics and automation (ICRA)

作者： Jiyuan Wang Chunyu Lin Lang Nie Shujun Huang Yao Zhao Xing Pan Rui Ai Institute of Information Science Beijing Jiaotong University Beijing China Haomo AI Technology Co. Ltd Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Depth estimation models have shown promising performance on clear scenes but fail to generalize to adverse weather conditions due to illumination variations, weather particles, etc. In this paper, we propose WeatherDepth, a self-supervised robust depth estimation model with curriculum contrastive learning, to tackle performance degradation in complex weather conditions. Concretely, we first present a progressive curriculum learning scheme with three simple-to-complex curricula to gradually adapt the model from clear to relative adverse, and then to adverse weather scenes. It encourages the model to gradually grasp beneficial depth cues against the weather effect, yielding smoother and better domain adaption. Meanwhile, to prevent the model from forgetting previous curricula, we integrate contrastive learning into different curricula. By drawing reference knowledge from the previous course, our strategy establishes a depth consistency constraint between different courses toward robust depth estimation in diverse weather. Besides, to reduce manual intervention and better adapt to different models, we designed an adaptive curriculum scheduler to automatically search for the best timing for course switching. In the experiment, the proposed solution is proven to be easily incorporated into various architectures and demonstrates state-of-the-art (SoTA) performance on both synthetic and real weather datasets. Source code and data are available at https://***/wangjiyuan9/WeatherDepth.

关键词： Degradation Adaptation models Source coding Estimation Contrastive learning Switches Manuals

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Virtual Force Calculation Model Based on an Improved Mass-Spring-Damper Systems

Virtual Force Calculation Model Based on an Improved Mass-Sp...

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robotics & Intelligent Manufacturing Technology (ISRIMT), international Symposium on

作者： Haoyang Li Nanyan Shen Jing Li Huanyu Yao School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798331528942

ISBN: (纸本)9798331528959

This paper studies a virtual force modeling approach based on an improved mass-spring-damper systems, aiming to establish a force feedback model that accurately simulates and predicts interactions between objects in a virtual environment using principles and methods of object kinematics. By integrating dynamics theory with computer simulation technology, this paper designs and implements a novel virtual force modeling approach. The model effectively handles interactions between virtual and real elements and provides virtual force feedback in complex environments. The research results indicate that the model performs excellently in enhancing the realism of virtual environments and improving user experience.

关键词： Analytical models Accuracy Computational modeling Force Dynamics Force feedback Virtual environments Kinematics Predictive models Springs

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Systematic review of a voice-to-Ecuadorian sign language translator

Systematic review of a voice-to-Ecuadorian sign language tra...

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international conference on Technological Innovation and AI Research (ICTIAIR 2025)

作者： Christian R. Salamea Palacios Fernando J. Viñanzaca Figueroa Michael A. Peralta Marin Interaction Robotics and Automation research group GIIRA Universidad Politécnica Salesiana Cuenca Ecuador

ISBN: (数字)9781837243235

In Ecuador, deaf people are facing several communication barriers due to the lack of tools for sign language translation. This study aims to make a systematic review of the current state of the field in Ecuador, analyzing how technologies have been applied to other translators in other countries, to have an approach of what is missing in Ecuador. Using the PRISMA methodology, searches were conducted in IEEE Xplore and Scielo. These findings highlight the use of artificial intelligence in these translators, also including 3D models, where most of this research is focused on India and the United States. Valuable information for the future development of a sign language translator for Ecuador.

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Curriculum-based Sensing Reduction in Simulation to Real-World Transfer for In-hand Manipulation

Curriculum-based Sensing Reduction in Simulation to Real-Wor...

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IEEE international conference on robotics and automation (ICRA)

作者： Lingfeng Tao Jiucai Zhang Qiaojie Zheng Xiaoli Zhang Oklahoma State University Stillwater OK USA GAC R&D Center Silicon Valley Sunnyvale CA USA Colorado School of Mines Intelligent Robotics and Systems Lab Golden CO USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Simulation to Real-World Transfer allows affordable and fast training of learning-based robots for manipulation tasks using Deep Reinforcement Learning methods. Currently, Asymmetric Actor-Critic approaches are used for Sim2Real to reduce the rich idealized features in simulation to the accessible ones in the real world. However, the feature reduction from the simulation to the real world is conducted through an empirically defined one-step curtail. Small feature reduction does not sufficiently remove the actor’s features, which may still cause difficulty setting up the physical system, while large feature reduction may cause difficulty and inefficiency in training. To address this issue, we proposed Curriculum-based Sensing Reduction to enable the actor to start with the same rich feature space as the critic and then get rid of the hard-to-extract features step-by-step for higher training performance and better adaptation for real-world feature space. The reduced features are replaced with random signals from a Deep Random Generator to remove the dependency between the output and the removed features and avoid creating new dependencies. The methods are evaluated on the Allegro robot hand in a real-world in-hand manipulation task. The results show that our methods have faster training and higher task performance than baselines and can solve real-world tasks when selected tactile features are reduced.

关键词： Training Robot sensing systems Deep reinforcement learning Generators Sensors Task analysis

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The Neural Network Algorithm for Decision Support During Control of the Welding Process by Robotic Complexes 15

The Neural Network Algorithm for Decision Support During Con...

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15th international conference Management of Large-Scale System Development, MLSD 2022

作者： Fominykh, Dmitry Fuchedzhi, Nikolai Kushnikov, Oleg Laboratory of System Problems of Control and Automation in Mechanical Engineering Institute for Precision Mechanics and Control Problems of Ras Saratov Russia V.A. Trapeznikov Institute of Control Sciences of Ras Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The article proposes an algorithm for solving the problem of controlling the welding process in robotic technological complexes according to the quality criterion of the products. The statement of the problem is given and the model of system dynamics by J. Forrester is considered. Welding quality indicators in robotic complexes were used as system levels. Functional dependencies are taken by using the mathematical apparatus of artificial neural networks, in particular, radial basis functions that allow for rigid interpolation. The introduction of the models and algorithms discussed in the article will improve the quality of products using robotic technological complexes. © 2022 IEEE.

关键词： robotics

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