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Gesture Control Method for Oral Blood Mixed Liquid Removal Robot*

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Gesture Control Method for Oral Blood Mixed Liquid Removal R...

WRC Symposium on Advanced robotics and Automation (WRC SARA)

作者： Xudong Ma Ziao Kuang Han Li Anqi Chen Changsheng Li Wenyong Liu Junchen Wang Yida David Hu Li Gao Bai-Quan Su Medical Robotics Laboratory School of Automation Beijing University of Posts and Tele-communications Beijing China School of Computer Beijing University of Posts and Tele-communications Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA Department of Periodontology National Stomatological Center Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases Beijing China

All of oral surgeries are accompanied by bleeding. Manual removal of blood is carried out by a nurse and dozens of repetitive blood suction operations are implemented by the nurse during a single oral surgery. In our previous work, a robot for removal of blood-water mixed liquid is designed. However, the robot requires an initial order to autonomous removal of blood mixed liquid. In this paper, a hand gesture control method for typical operations of the robot is developed. Gesture dataset are developed at first and a deep learning neural network is trained for detection of hand gesture. The gesture control procedures for four typical operations are provided in detail. In a group of experiments, it’s demonstrated that an oral surgeon controls the robot’s motion by presenting prescribed hand gesture. The proposed method could be an alternative manner for blood mixed liquid removal.

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A heterogeneous group CNN for image super-resolution

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arXiv 2022年

作者： Tian, Chunwei Zhang, Yanning Zuo, Wangmeng Lin, Chia-Wen Zhang, David Yuan, Yixuan The School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China The School of Computer Science Northwestern Polytechnical University The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China The School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Guangdong Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong

Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image superresolution (SR) under complex scenes. In this paper, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image. Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance internal and external relations of different channels for facilitating richer low-frequency structure information of different types. To prevent appearance of obtained redundant features, a refinement block with signal enhancements in a serial way is designed to filter useless information. To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN. Besides, a parallel up-sampling mechanism is developed to train a blind SR model. Extensive experiments illustrate that the proposed HGSRCNN has obtained excellent SR performance in terms of both quantitative and qualitative analysis. Codes can be accessed at https://***/hellloxiaotian/HGSRCNN. © 2022, CC BY-NC-SA.

关键词： Convolutional neural networks

Clustering of the Blendshape facial model

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arXiv 2021年

作者： Racković, Stevo Soares, Cláudia Jakovetić, Dušan Desnica, Zoranka Ljubobratović, Relja Institute for Systems and Robotics Instituto Superior Técnico Lisbon Portugal Computer Science Department NOVA School of Science and Technology Lisbon Portugal Department of Mathematics Faculty of Sciences UNS Novi Sad Serbia 3Lateral Epic Games Company

Digital human animation relies on high-quality 3D models of the human face—rigs. A face rig must be accurate and, at the same time, fast to compute. One of the most common rigging models is the blendshape model. We present a novel approach for learning the inverse rig parameters at increased accuracy and decreased computational cost at the same time. It is based on a two fold clustering of the blendshape face model. Our method focuses exclusively on the underlying space of deformation and produces clusters in both the mesh space and the controller space—something that was not investigated in previous literature. This segmentation finds intuitive and meaningful connections between groups of vertices on the face and deformation controls, and further these segments can be observed independently. A separate model for solving the inverse rig problem is then learnt for each segment. Our method is completely unsupervised and highly parallelizable. © 2021, CC BY-NC-SA.

关键词： Deformation

Beyond game environments: Evolutionary algorithms with parameter space noise for task-oriented dialogue policy exploration

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Neurocomputing 2025年 648卷

作者： Qingxin Xiao Yangyang Zhao Lingwei Dang Yun Hao Le Che Qingyao Wu South China University of Technology School of Software Engineering Guangzhou 510006 Guangdong PR China Institute for Super Robotics(Huangpu) Guangzhou 510700 Guangdong PR China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha 410114 Hunan PR China South China University of Technology School of Architecture Guangzhou 510006 Guangdong PR China

Reinforcement learning (RL) has achieved significant success in task-oriented dialogue (TOD) policy learning. Nevertheless, training dialogue policy through RL faces a critical challenge: insufficient exploration, which leads to the policy getting trapped in local optima. Evolutionary algorithms (EAs) enhance exploration breadth by maintaining and selecting diverse individuals, and they often add parameter space noise among different individuals to simulate mutation, thereby increasing exploration depth. This approach has proven to be an effective method for enhancing RL exploration and has shown promising results in game domains. However, previous research has not analyzed its effectiveness in TOD dialogue policy. Given the substantial differences between gaming contexts and TOD dialogue policy, this paper explores and validates the efficacy of EAs in TOD dialogue policy, investigating the effects of different evolutionary cycles and various noise strategies across different dialogue tasks to determine which combination of evolutionary cycle and noise strategy is most suitable for TOD dialogue policy. Additionally, we propose an adaptive noise evolution method that dynamically adjusts noise scales to improve exploration efficiency. Experiments on the MultiWOZ dataset demonstrate significant performance improvements, achieving state-of-the-art results in both on-policy and off-policy settings.

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An Efficient Ant Colony System for Multi-Robot Task Allocation with Large-scale Cooperative Tasks and Precedence Constraints

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An Efficient Ant Colony System for Multi-Robot Task Allocati...

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Xiao-Fang Liu Bo-Cheng Lin Zhi-Hui Zhan Sang-Woon Jeon Jun Zhang Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China Hanyang University Ansan South Korea Chaoyang University of Technology Taichung Taiwan China

ISBN: (纸本)9781728190495

Multiple heterogeneous robots can work together to execute complex tasks. Given multiple heterogeneous tasks and heterogeneous robots, the allocation of tasks to robots is a challenging optimization problem. Lots of methods have been proposed for the multi-robot task allocation (MRTA) problem. However, most existing methods only consider small-scale tasks without precedence constraints. Hence, this paper tracks the time-extended MRTA problem with large-scale cooperative tasks and precedence constraints, and proposes an efficient ant colony system (ACS) to solve the problem. In the proposed algorithm, we adopt a permutation with task-robot alliance pairs as the encode scheme to represent a feasible solution. A pheromone matrix is initialized by a hierarchical greedy strategy and iteratively updated to record historical experience. Heuristic information related to the optimization objective is also designed to help algorithm find better solutions according to the current state. Through combining pheromone and heuristic information, the ACS is able to search high-quality solutions from a global perspective. Experimental results on multiple problem instances are reported to show the advantage of the proposed method. The proposed ACS method can well solve the MRTA problem with large-scale cooperative tasks and precedence constraints.

关键词： Processor scheduling Heuristic algorithms Resource management Task analysis Robots Optimization Computational intelligence

Learning Enhanced Resolution-Wise Features For Human Pose Estimation

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Learning Enhanced Resolution-Wise Features For Human Pose Es...

IEEE International Conference on Image Processing

作者： Kun. Zhang Peng He Ping Yao Ge Chen Rui Wu Min Du Huimin Li Li Fu Tianyao Zheng Chinese Academy of Sciences Institute of Computing Technology School of Computer Science and Technology University of Chinese Academy of Sciences Horizon Robotics School of Automation Beijing Institute of Technology

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Recently, multi-resolution networks (such as Hourglass, CPN, HRNet, etc.) have achieved significant performance on pose estimation by combining feature maps of various resolutions. In this paper, we propose a Resolution-wise Attention Module (RAM) and Gradual Pyramid Refinement (GPR), to learn enhanced resolution-wise feature maps for precise pose estimation. Specifically, RAM learns a group of weights to represent the different importance of feature maps across resolutions, and the GPR gradually merges every two feature maps from low to high resolutions to regress final human keypoint heatmaps. With the enhanced resolution-wise features learnt by CNN, we obtain more accurate human keypoint locations. The efficacies of our proposed methods are demonstrated on MS-COCO dataset, achieving state-of-the-art performance with average precision of 77.7 on COCO va12017 set and 77.0 on test-dev2017 set without using extra human keypoint training dataset.

关键词： Pose estimation Heating systems Ground penetrating radar Feature extraction Random access memory Training Task analysis

Learning to solve hard minimal problems

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arXiv 2021年

作者： Hruby, Petr Duff, Timothy Leykin, Anton Pajdla, Tomas ETH Zürich Department of Computer Science University of Washington Department of Mathematics United States Georgia Institute of Technology School of Mathematics Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Republic

We present an approach to solving hard geometric optimization problems in the RANSAC framework. The hard minimal problems arise from relaxing the original geometric optimization problem into a minimal problem with many spurious solutions. Our approach avoids computing large numbers of spurious solutions. We design a learning strategy for selecting a starting problem-solution pair that can be numerically continued to the problem and the solution of interest. We demonstrate our approach by developing a RANSAC solver for the problem of computing the relative pose of three calibrated cameras, via a minimal relaxation using four points in each view. On average, we can solve a single problem in under 70 µs. We also benchmark and study our engineering choices on the very familiar problem of computing the relative pose of two calibrated cameras, via the minimal case of five points in two *** Codes 65H20, 68T45 © 2021, CC BY.

关键词： Optimization

Image super-resolution with an enhanced group convolutional neural network

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arXiv 2022年

作者： Tian, Chunwei Yuan, Yixuan Zhang, Shichao Lin, Chia-Wen Zuo, Wangmeng Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://***/hellloxiaotian/ESRGCNN. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

Lazy lifelong planning for efficient replanning in graphs with expensive edge evaluation

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arXiv 2021年

作者： Lim, Jaein Srinivasa, Siddhartha Tsiotras, Panagiotis School of Aerospace Engineering Georgia Institute of Technology AtlantaGA30332-0150 United States School of Computer Science & Engineering University of Washington SeattleWA98195-2355 United States School of Aerospace Engineering Institute for Robotics Intelligent Machines Georgia Institute of Technology AtlantaGA30332-0150 United States

We present an incremental search algorithm, called Lifelong-GLS, which combines the vertex efficiency of Lifelong Planning A* (LPA*) and the edge efficiency of Generalized Lazy Search (GLS) for efficient replanning on dynamic graphs where edge evaluation is expensive. We use a lazily evaluated LPA* to repair the cost-to-come inconsistencies of the relevant region of the current search tree based on the previous search results, and then we restrict the expensive edge evaluations only to the current shortest subpath as in the GLS framework. The proposed algorithm is complete and correct in finding the optimal solution in the current graph, if one exists. We also show that the search returns a bounded suboptimal solution, if an inflated heuristic edge weight is used and the tree repairing propagation is truncated early for faster search. Finally, we show the efficiency of the proposed algorithm compared to the standard LPA* and the GLS algorithms over consecutive search episodes in a dynamic environment. For each search, the proposed algorithm reduces the edge evaluations by a significant amount compared to the LPA*. Both the number of vertex expansions and the number of edge evaluations are reduced substantially compared to GLS, as the proposed algorithm utilizes previous search results to facilitate the new search. Copyright © 2021, The Authors. All rights reserved.

关键词： Efficiency