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A self-supervised learning-based 6-DOF grasp planning method for manipulator

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arXiv 2021年

作者： Peng, Gang Ren, Zhenyu Wang, Hao Li, Xinde School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China IEEE senior member School of Automation South East University Nanjing China

To realize a robust robotic grasping system for unknown objects in an unstructured environment, large amounts of grasp data and 3D model data for the object are required, the sizes of which directly affect the rate of successful grasps. To reduce the time cost of data acquisition and labeling and increase the rate of successful grasps, we developed a self-supervised learning mechanism to control grasp tasks performed by manipulators. First, a manipulator automatically collects the point cloud for the objects from multiple perspectives to increase the efficiency of data acquisition. The complete point cloud for the objects is obtained by utilizing the hand-eye vision of the manipulator, and the TSDF algorithm. Then, the point cloud data for the objects is used to generate a series of six-degrees-of-freedom grasp poses, and the force-closure decision algorithm is used to add the grasp quality label to each grasp pose to realize the automatic labeling of grasp data. Finally, the point cloud in the gripper closing area corresponding to each grasp pose is obtained;it is then used to train the grasp-quality classification model for the manipulator. The results of data acquisition experiments demonstrate that the proposed method allows high-quality data to be obtained. The simulated results prove the effectiveness of the proposed grasp-data acquisition method. The results of performing actual grasping experiments demonstrate that the proposed self-supervised learning method can increase the rate of successful grasps for the manipulator. Copyright © 2021, The Authors. All rights reserved.

关键词： Manipulators

CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition

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arXiv 2025年

作者： Kan, Siyuan Wu, Huanyu Cui, Zhenyao Huang, Fan Xu, Xiaolong Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Wuhan Institute of Digital Engineering Wuhan430074 China Shanghai Jiao Tong University Shanghai200240 China

Emotion recognition is an important component of affective computing, and also human-machine interaction. Unimodal emotion recognition is convenient, but the accuracy may not be high enough;on the contrary, multi-modal emotion recognition may be more accurate, but it also increases the complexity and cost of the data collection system. This paper considers cross-modal emotion recognition, i.e., using both electroencephalography (EEG) and eye movement in training, but only EEG or eye movement in test. We propose cross-modal contrastive representation distillation (CMCRD), which uses a pre-trained eye movement classification model to assist the training of an EEG classification model, improving feature extraction from EEG signals, or vice versa. During test, only EEG signals (or eye movement signals) are acquired, eliminating the need for multi-modal data. CMCRD not only improves the emotion recognition accuracy, but also makes the system more simplified and practical. Experiments using three different neural network architectures on three multi-modal emotion recognition datasets demonstrated the effectiveness of CMCRD. Compared with the EEG-only model, it improved the average classification accuracy by about 6.2%. © 2025, CC BY.

关键词： Modal analysis

Surface Defect Detection Based on Deep Learning and Collaborative Cloud-edge Computation

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Surface Defect Detection Based on Deep Learning and Collabor...

International Conference on Computer and Communications (ICCC)

作者： Shunjie He Bo Yang Dafeng Zhu Cheng Li Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665409513

Surface defect detection is a fundamental step of quality management. Nowadays, surface defect detection system based on machine vision technology has made great progress which can gradually replace the human inspector. With the help of convolutional neural networks, the accuracy and stability of the surface defect detection algorithm are improving rapidly. However, it is difficult to process the images which have small defect areas and complex backgrounds. Furthermore, lacking defective samples and limited computation resources are also tough problems. To deal with these problems, a two-stage algorithm combined with object detection and classification networks is proposed. Transform learning and data augmentation are used to deal with the problem of lacking defect samples. Besides, a detection system based on collaborative cloud-edge computation is designed to improve the performance of the algorithm. The result shows that the proposed method has high performance and low requirements of computation resources.

关键词： Training Collaboration Transforms Object detection Classification algorithms Convolutional neural networks Detection algorithms

A Multi-scale Network for Semantic Segmentation of 3D Point Clouds

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A Multi-scale Network for Semantic Segmentation of 3D Point ...

Chinese Automation Congress (CAC)

作者： Ying He Li Xiao Yong Jiang Zhigang Sun Zhuo Wang Gang Peng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Semantic segmentation is a fundamental operation in scene analysis. In this paper, an effective multiscale network for 3D point cloud semantic segmentation was introduced. By using a multiscale local feature extraction module which composed of four feature extractors of different scales in parallel, the generalizability of network for complex structures is enhanced effectively. To adaptively learn important feature channels, an attention mechanism is designed. Combining multiple features through skip connection, the network can preferably assign the semantic label for every point by exploiting global and local features. Experiments on 3D dataset (S3DIS) verify that our network is able to learn local region features, and the results are superior or comparable to the state-of-the-art.

关键词： Feature extraction Three-dimensional displays Semantics Two dimensional displays intelligent control image processing Automation

Distributed second order sliding mode secondary frequency control for HVDC system

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Distributed second order sliding mode secondary frequency co...

Chinese Automation Congress (CAC)

作者： Yang-Ming Dou Ming Chi Zhi-Wei Liu Key Laboratory of Image Processing and Intelligent Control Ministry of Education College of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper studies the secondary frequency control among non-synchronous AC areas interconnected by High Voltage Direct Current (HVDC). A distributed second order sliding mode control scheme is adopted to secondary frequency control of HVDC transmission systems to adjust the frequency of power grid to rated value, which solves the frequency disturbance caused by load power change of the DC power grid. And on this basis, the power generation in each region is reasonably distributed, so as to minimize the cost of power generation. Finally, the stability of the system is proved on an appropriate sliding manifold.

关键词： Frequency control HVDC transmission Power system stability Economics Sliding mode control Power system dynamics Manifolds

Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

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arXiv 2020年

作者： Liu, Ziang Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

—In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria – informativeness, representativeness, and diversity – have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

A memristor-based generalization and differentiation circuit design and the application in recognition

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A memristor-based generalization and differentiation circuit...

第三十九届中国控制会议

作者： Meijia Shang Xiaoping Wang Mian Li the School of Artificial Intelligence and Automation Huazhong University of Science and Technology IEEE the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China

The traditional Pavlov associative memory circuit realizes the law of learning and forgetting in classical conditioned reflex. In addition, the law of generalization and differentiation also belongs to classical conditioned reflex, so the process of associative memory can be more effectively simulated by adding generalization and differentiation theory on the basis of traditional associative memory. In this paper, a memristor-based circuit is designed to implement generalization and differentiation based on Pavlov associative memory. The circuit can be applied to simple classification recognition. Based on the features of objects as input, the output of the circuit is used as the classification result to achieve the function of classification and recognition. Finally,the accuracy of classification recognition on the generalization and differentiation circuit proposed in this paper can be verified by the simulation results in PSPICE.

关键词： Memristor generalization differentiation Pavlov associative memory recognition

Empirical studies on the properties of linear regions in deep neural networks

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arXiv 2020年

作者： Zhang, Xiao Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

A deep neural network (DNN) with piecewise linear activations can partition the input space into numerous small linear regions, where different linear functions are fitted. It is believed that the number of these regions represents the expressivity of the DNN. This paper provides a novel and meticulous perspective to look into DNNs: Instead of just counting the number of the linear regions, we study their local properties, such as the inspheres, the directions of the corresponding hyperplanes, the decision boundaries, and the relevance of the surrounding regions. We empirically observed that different optimization techniques lead to completely different linear regions, even though they result in similar classification accuracies. We hope our study can inspire the design of novel optimization techniques, and help discover and analyze the behaviors of DNNs. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

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Integrating Informativeness, Representativeness and Diversit...

International Joint Conference on Neural Networks (IJCNN)

作者： Ziang Liu Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria - informativeness, representativeness, and diversity - have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness.

关键词： Computational modeling Clustering algorithms Machine learning Training Labeling Predictive models image processing