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Learning compliant grasping and manipulation by teleoperation with adaptive force control

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arXiv 2021年

作者： Zeng, Chao Li, Shuang Jiang, Yiming Li, Qiang Chen, Zhaopeng Yang, Chenguang Zhang, Jianwei Department of Informatics Universiẗat Hamburg Germany National Engineering Laboratory for Robot Visual Perception and Control Hunan University China Center for Cognitive Interaction Technology Bielefeld University Germany Bristol Robotics Laboratory University of the West of England United Kingdom

In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multifingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angle trajectories. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment in a compliant manner. Our approach has been verified in multiple tasks in physics simulation, i.e., grasping, opening-a-door, turning-a-cap, and touching-a-mouse, and has shown more reliable performances than the existing position control and the fixed-gain-based force control approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Remote control

Efficient Recovery of Multi-Camera Motion from Two Affine Correspondences

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Efficient Recovery of Multi-Camera Motion from Two Affine Co...

IEEE International Conference on robotics and Automation (ICRA)

作者： Banglei Guan Ji Zhao Daniel Barath Friedrich Fraundorfer College of Aerospace Science and Engineering National University of Defense Technology China Centre for Machine Perception Czech Technical University Czech Republic Machine Perception Research Laboratory MTA SZTAKI Hungary Institute for Computer Graphics and Vision Graz University of Technology Austria Remote Sensing Technology Institute German Aerospace Center Germany

We propose an efficient method to estimate the relative pose of a multi-camera system from a minimum of two affine correspondences (ACs). Our solution is novel as it computes the 6DOF relative pose by utilizing a first-order rotation approximation. We directly derive a single polynomial based on the constraint between ACs and the generalized camera model. Then a closed-form solution is found analytically and it produces an accurate relative pose estimation efficiently. Benefiting from the low number of exploited correspondences and the speed of the solver, it speeds up robust estimators, e.g. RANSAC, significantly. The proposed method is evaluated both on synthetic data and real-world image sequences from the KITTI benchmark. It is shown that the proposed solver is superior to the state-of-the-art algorithms in terms of accuracy.

关键词： Closed-form solutions Automation Computational modeling Roads Conferences Pose estimation Benchmark testing

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

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Nano-Micro Letters 2023年第12期15卷 161-187页

作者： Jin Li Naiteng Wu Jian Zhang Hong‑Hui Wu Kunming Pan Yingxue Wang Guilong Liu Xianming Liu Zhenpeng Yao Qiaobao Zhang College of Chemistry and Chemical Engineering and Henan Key Laboratory of Function‑Oriented Porous MaterialsLuoyang Normal UniversityLuoyang 471934People’s Republic of China New Energy Technology Engineering Lab of Jiangsu Province College of ScienceNanjing University of Posts and Telecommunications(NUPT)Nanjing 210023People’s Republic of China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083People’s Republic of China Department of Chemistry University of Nebraska-LincolnLincolnNE 8588USA Henan Key Laboratory of High‑Temperature Structural and Functional Materials National Joint Engineering Research Center for Abrasion Control and Molding of Metal MaterialsHenan University of Science and TechnologyLuoyang 471003People’s Republic of China National Engineering Laboratory for Risk Perception and Prevention Beijing 100041People’s Republic of China Center of Hydrogen Science Shanghai Jiao Tong UniversityShanghai 200000People’s Republic of China State Key Laboratory of Metal Matrix Composites School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200000People’s Republic of China State Key Laboratory of Physical Chemistry of Solid Surfaces College of MaterialsXiamen UniversityXiamen 361005People’s Republic of China

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing ***,the conventional"trial and error"method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and ***,the advancement of machine learning brings new opportunities for electrocatalysts discovery and *** analyzing experimental and theoretical data,machine learning can effectively predict their hydrogen evolution reaction(HER)*** review summarizes recent developments in machine learning for low-dimensional electrocatalysts,including zero-dimension nanoparticles and nanoclusters,one-dimensional nanotubes and nanowires,two-dimensional nanosheets,as well as other *** particular,the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are ***,the future directions and perspectives for machine learning in electrocatalysis are discussed,emphasizing the potential for machine learning to accelerate electrocatalyst discovery,optimize their performance,and provide new insights into electrocatalytic ***,this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

关键词： Machine learning Hydrogen evolution reaction Low-dimensional electrocatalyst Descriptor Algorithm

Globally-Optimal Event Camera Motion Estimation 1

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16th European Conference on Computer vision, ECCV 2020

作者： Peng, Xin Wang, Yifu Gao, Ling Kneip, Laurent Mobile Perception Lab SIST ShanghaiTech University Shanghai China Shanghai Institute of Microsystems and Information Technology Chinese Academy of Sciences Beijing China Australian National University Canberra Australia Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

ISBN: (数字)9783030585747

ISBN: (纸本)9783030585730

Event cameras are bio-inspired sensors that perform well in HDR conditions and have high temporal resolution. However, different from traditional frame-based cameras, event cameras measure asynchronous pixel-level brightness changes and return them in a highly discretised format, hence new algorithms are needed. The present paper looks at fronto-parallel motion estimation of an event camera. The flow of the events is modeled by a general homographic warping in a space-time volume, and the objective is formulated as a maximisation of contrast within the image of unwarped events. However, in stark contrast to prior art, we derive a globally optimal solution to this generally non-convex problem, and thus remove the dependency on a good initial guess. Our algorithm relies on branch-and-bound optimisation for which we derive novel, recursive upper and lower bounds for six different contrast estimation functions. The practical validity of our approach is supported by a highly successful application to AGV motion estimation with a downward facing event camera, a challenging scenario in which the sensor experiences fronto-parallel motion in front of noisy, fast moving textures. © 2020, Springer Nature Switzerland AG.

关键词： Motion estimation

MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Zheng, Jin Geng, Zichen Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering China School of Robotics Hunan University Changsha410082 China Central South University Changsha410082 China Lancaster University LA1 4YW United Kingdom The University of Western Australia WA6009 Australia

Object pose estimation is a core means for robots to understand and interact with their environment. For this task, monocular category-level methods are attractive as they require only a single RGB camera. However, current methods rely on shape priors or CAD models of the intra-class known objects. We propose a diffusion-based monocular category-level 9D object pose generation method, MonoDiff9D. Our motivation is to leverage the probabilistic nature of diffusion models to alleviate the need for shape priors, CAD models, or depth sensors for intra-class unknown object pose estimation. We first estimate coarse depth via DINOv2 from the monocular image in a zero-shot manner and convert it into a point cloud. We then fuse the global features of the point cloud with the input image and use the fused features along with the encoded time step to condition MonoDiff9D. Finally, we design a transformer-based denoiser to recover the object pose from Gaussian noise. Extensive experiments on two popular benchmark datasets show that MonoDiff9D achieves state-of-the-art monocular category-level 9D object pose estimation accuracy without the need for shape priors or CAD models at any stage. Our code will be made public at https://***/CNJianLiu/MonoDiff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

2023 IEEE/CVF Conference on Computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

XGBoost and CNN-based prediction of external forces and stress-strain during solidification reduction in casting

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Progress in Natural Science: Materials International 2025年

作者： Yi Nian Chaojie Zhang Xinyu Tang Youcheng Zong Jiale Li Liqiang Zhang Yingxue Wang School of Metallurgical Engineering Anhui University of Technology Maanshan 243032 Anhui China College of Information Science and Engineering Northeastern University Shenyang 110057 Liaoning China National Engineering Research Center for Public Safety Risk Perception and Control by Big Data (RPP) China Academic of Electronics and Information Technology Shuangyuan Street Beijing 100043 China

The continuous casting reduction process can effectively improve defects such as central segregation and porosity in the billets, making it a key process for enhancing billets quality. This study aims to investigate the required external force during the solidification reduction process and the equivalent stress-strain distribution at various positions inside the billets, and to construct predictive models for equivalent stress-strain and external force based on the reduction amount. Through conducting laboratory ingot solidification process reduction tests and finite element numerical simulations, the corresponding parameters including external force, reduction amount, equivalent stress, and equivalent strain were obtained. Data augmentation and random noise processing were applied to the parameter features, and predictive models for external force using the XGBoost algorithm and for equivalent stress-strain using a convolutional neural networks were constructed. The results indicate that as the reduction amount increases, the required external force increases, and the equivalent stress-strain inside the billet is much higher than at the surface. Furthermore, in the external force prediction model, the MAPE value of the XGBoost algorithm is 8.15 %, while in the equivalent stress-strain model, the R 2 value of the convolutional neural networks training set is 0.92. These results demonstrate that both the external force prediction model and the equivalent stress-strain prediction model exhibit high accuracy, and robustness in predictive tasks.

关键词：

SoccerNet 2024 Challenges Results

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arXiv 2024年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Joos, Victor Magera, Floriane Held, Jan Ghasemzadeh, Seyed Abolfazl Zhou, Xin Seweryn, Karolina Kowalczyk, Mateusz Mróz, Zuzanna Lukasik, Szymon Halon, Michal Mkhallati, Hassan Deliège, Adrien Hinojosa, Carlos Sanchez, Karen Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Gorski, Adam Clapés, Albert Boiarov, Andrei Afanasiev, Anton Xarles, Artur Scott, Atom Lim, ByoungKwon Yeung, Calvin Gonzalez, Cristian Rüfenacht, Dominic Pacilio, Enzo Deuser, Fabian Altawijri, Faisal Sami Cachón, Francisco Kim, HanKyul Wang, Haobo Choe, Hyeonmin Kim, Hyunwoo J. Kim, Il-Min Kang, Jae-Mo Tursunboev, Jamshid Yang, Jian Hong, Jihwan Lee, Jimin Zhang, Jing Lee, Junseok Zhang, Kexin Habel, Konrad Jiao, Licheng Li, Linyi Gutiérrez-Pérez, Marc Ortega, Marcelo Li, Menglong Lopatto, Milosz Kasatkin, Nikita Nemtsev, Nikolay Oswald, Norbert Udin, Oleg Kononov, Pavel Geng, Pei Alotaibi, Saad Ghazai Kim, Sehyung Ulasen, Sergei Escalera, Sergio Zhang, Shanshan Yang, Shuyuan Moon, Sunghwan Moeslund, Thomas B. Shandyba, Vasyl Golovkin, Vladimir Dai, Wei Chung, WonTaek Liu, Xinyu Zhu, Yongqiang Kim, Youngseo Li, Yuan Yang, Yuting Xiao, Yuxuan Cheng, Zehua Li, Zhihao Belgium King Abdullah University of Science and Technology Saudi Arabia Sportradar Switzerland UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States NASK National Research Institute Poland Belgium Sharif University of Technology Iran Footovision France Mobius Labs Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain TAHAKOM Saudi Arabia AIBrain Eidos.ai United Kingdom School of Computer Science and Engineering Nanjing University of Science and Technology China Constructor.tech Universitat de Barcelona Spain Computer Vision Center Spain Aalborg University Denmark Kyungpook National University Korea Republic of Intelligent Perception and Image Understanding Lab Xidian University China Germany Korea University Korea Republic of Warsaw University of Technology Poland University of Oxford United Kingdom China Science IntelliCloud Technology Co. Ltd China Robo Space Graduate School of Informatics Nagoya University Japan Tongji University China AGH University of Krakow Poland

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://***, with baselines and development kits available at https://***/SoccerNet. © 2024, CC BY.

关键词： HTTP

SoccerNet 2023 Challenges Results

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arXiv 2023年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Magera, Floriane Zhou, Xin Mkhallati, Hassan Deliège, Adrien Held, Jan Hinojosa, Carlos Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Kamal, Abdullah Maglo, Adrien Clapés, Albert Abdelaziz, Amr Xarles, Artur Orcesi, Astrid Scott, Atom Liu, Bin Lim, Byoungkwon Chen, Chen Deuser, Fabian Yan, Feng Yu, Fufu Shitrit, Gal Wang, Guanshuo Choi, Gyusik Kim, Hankyul Guo, Hao Fahrudin, Hasby Koguchi, Hidenari Ardö, Håkan Salah, Ibrahim Yerushalmy, Ido Muhammad, Iftikar Uchida, Ikuma Be'ery, Ishay Rabarisoa, Jaonary Lee, Jeongae Fu, Jiajun Yin, Jianqin Xu, Jinghang Nang, Jongho Denize, Julien Li, Junjie Zhang, Junpei Kim, Juntae Synowiec, Kamil Kobayashi, Kenji Zhang, Kexin Habel, Konrad Nakajima, Kota Jiao, Licheng Ma, Lin Wang, Lizhi Wang, Luping Li, Menglong Zhou, Mengying Nasr, Mohamed Abdelwahed, Mohamed Liashuha, Mykola Falaleev, Nikolay Oswald, Norbert Jia, Qiong Pham, Quoc-Cuong Song, Ran Hérault, Romain Peng, Rui Chen, Ruilong Liu, Ruixuan Baikulov, Ruslan Fukushima, Ryuto Escalera, Sergio Lee, Seungcheon Chen, Shimin Ding, Shouhong Someya, Taiga Moeslund, Thomas B. Li, Tianjiao Shen, Wei Zhang, Wei Li, Wei Dai, Wei Luo, Weixin Zhao, Wending Zhang, Wenjie Yang, Xinquan Ma, Yanbiao Joo, Yeeun Zeng, Yingsen Gan, Yiyang Zhu, Yongqiang Zhong, Yujie Ruan, Zheng Li, Zhiheng Huang, Zhijian Meng, Ziyu Belgium Saudi Arabia Sportradar Norway UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States Belgium Sharif University of Technology Iran Footovision France Zewail City of Science Technology and Innovation Egypt Université Paris-Saclay CEA France Universitat de Barcelona Spain Computer Vision Center Spain Nagoya University Japan Research Center for Applied Mathematics and Machine Intelligence Zhejiang Lab China AIBrain United States OPPO Research Institute China Germany Meituan China Tencent Youtu Lab China Amazon Prime Video Sport United States Sogang University Korea Republic of The University of Tokyo Japan Spiideo Sweden University of Tsukuba Japan School of Artificial Intelligence Beijing University of Posts and Telecommunications China Normandie Univ INSA Rouen LITIS France Shanghai Jiao Tong University China Key Laboratory of Intelligent Perception and Image Understanding The Ministry of Education Xidian University China NASK - National Research Institute Poland Robo Space China Tongji University China Sportlight Technology United Kingdom School of Control Science and Engineering Shandong University China lRomul Russia Aalborg University Denmark Turing AI Cultures GmbH Germany Information Systems Technology and Design Singapore University of Technology and Design Singapore Sun Yat-sen University China

The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leader-boards are available on https://***. Baselines and development kits can be found on https://***/SoccerNet. © 2023, CC BY.

关键词： Computer vision