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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Li, Xiaocan Cai, Yinghao Wang, Shuo Lu, Tao Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781728163215

We exploit the embedding ability of a de-noising autoencoder for an implicit 3D rotation representation learning at the category level. Contrast to the exact 3D reconstruction model of each instance-level physical object, we leverage the inexact CAD/Reconstruction models of an object as the representative model for some category. Under our assumptions that objects within the same category share resemble geometry, we train a de-noising autoencoder on synthetic 3D views of category-level objects to extract the homogenous features at the bottleneck layer. The latent representation is agnostic not only to heterogeneous textures, colors, and illuminations, but also ambiguous pose caused by object symmetry. To extend the instance-level 3D translation estimation to the category level, we considered the 3D diagonal length ratio between the source and target object. We achieved a frame rate of 17Hz. © 2019 IEEE.

关键词： Object detection

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Parameter estimation survey for multi-joint robot dynamic calibration case study

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中国科学:化学（英文版） 2019年第10期62卷 98-112页

作者： Shaolin ZHANG Shuo WANG Fengshui JING Min TAN The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031 China

Accurate model parameters are the basis of robot *** linear and nonlinear models have been proposed to calibrate the inertial parameters and friction parameters of multi-joint ***,methods of choosing a model and calculating its parameters still have few *** paper reviews typical linear/nonlinear models and different calculation methods for robot dynamic *** simulations,the features of different methods are analyzed,including torque error,parameter error,model adaptability,solution time,and anti-interference ability of the calibration ***,an experiment performed on a six-degree-of-freedom industrial manipulator is used as an example to illustrate how to select the model for a specified *** comparisons and experiments provide references for the parameter calibration of multi-joint robots.

关键词： dynamic parameter calibration friction calibration robot dynamics industrial manipulator dynamic models

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Modeling and analysis of an underwater biomimetic vehicle-manipulator system

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Science China(Information Sciences) 2022年第3期65卷 255-257页

作者： Xuejian BAI Yu WANG Shuo WANG Rui WANG Min TAN Wei WANG School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology School of Control Science and Engineering Shandong University

In recent years, underwater vehicle-manipulator systems(UVMSs) have captured researchers' attention. Inspired by fish, bionic mechanisms were introduced into a *** underwater biomimetic vehicle-manipulator system(UBVMS) is actuated by two symmetrically arranged biomimetic undulatory fin propulsors [1]. Based on the UBVMS, some topics have been researched, such as freefloating autonomous operation [2, 3], three-dimensional helical path following [4], and underwater video processing [5].

关键词： underwater biomimetic vehicle underwater robots underwater vehicle-manipulator system biomimetic robots hydrodynamic modeling

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Programming by visual demonstration for pick-and-place tasks using robot skills

Programming by visual demonstration for pick-and-place tasks...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Hao, Peng Lu, Tao Cai, Yinghao Wang, Shuo State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781728163215

In this paper, we present a vision-based robot programming system for pick-and-place tasks that can generate programs from human demonstrations. The system consists of a detection network and a program generation module. The detection network leverages convolutional pose machines to detect the key-points of the objects. The network is trained in a simulation environment in which the train set is collected and auto-labeled. To bridge the gap between reality and simulation, we propose a design method of transform function for mapping a real image to synthesized style. Compared with the unmapped results, the Mean Absolute Error (MAE) of the model completely trained with synthesized images is reduced by 23% and the False Negative Rate FNR (FNR) of the model fine-tuned by the real images is reduced by 42.5% after mapping. The program generation module provides a human-readable program based on the detection results to reproduce a real-world demonstration, in which a longshort memory (LSM) is designed to integrate current and historical information. The system is tested in the real world with a UR5 robot on the task of stacking colored cubes in different orders. © 2019 IEEE.

关键词： Demonstrations

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Lightweight Double Attention-Fused Networks for Intraoperative Stent Segmentation 23rd

Lightweight Double Attention-Fused Networks for Intraoperati...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhou, Yan-Jie Xie, Xiao-Liang Hou, Zeng-Guang Zhou, Xiao-Hu Bian, Gui-Bin Liu, Shi-Qi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030597245

In endovascular interventional therapy, the fusion of preoperative data with intraoperative X-ray fluoroscopy has demonstrated the potential to reduce radiation dose, contrast agent and processing time. Real-time intraoperative stent segmentation is an important pre-requisite for accurate fusion. Nevertheless, this task often comes with the challenge of the thin stent wires with low contrast in noisy X-ray fluoroscopy. In this paper, a novel and efficient network, termed Lightweight Double Attention-fused Network (LDA-Net), is proposed for end-to-end stent segmentation in intraoperative X-ray fluoroscopy. The proposed LDA-Net consists of three major components, namely feature attention module, relevance attention module and pre-trained MobileNetV2 encoder. Besides, a hybrid loss function of both reinforced focal loss and dice loss is designed to better address the issues of class imbalance and misclassified examples. Quantitative and qualitative evaluations on 175 intraoperative X-ray sequences demonstrate that the proposed LDA-Net significantly outperforms simpler baselines as well as the best previously-published result for this task, achieving the state-of-the-art performance. © 2020, Springer Nature Switzerland AG.

关键词： Stents

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GPR and SPSO-CG based gait pattern generation for subject-specific training

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Science China(Information Sciences) 2021年第8期64卷 244-246页

作者： Weiqun WANG Weiguo SHI Shixin REN Zeng-Guang HOU Xu LIANG Jiaxin WANG Liang PENG The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Dear editor,Gait training has been proved effective for recovery of walking ability for nerve injury patients caused by stroke, spinal cord injury(SCI), traumatic brain injury(TBI), etc. The traditional gait training methods, usually carried out by hands, are laborious and time-consuming, and hence, the training intensity is difficult to be maintained and the rehabilitation results are not satisfactory.

关键词： GPR and SPSO-CG based gait pattern generation for subject-specific training

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Locomotion control of a hybrid propulsion biomimetic underwater vehicle via deep reinforcement learning

Locomotion control of a hybrid propulsion biomimetic underwa...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zhang, Tiandong Wang, Rui Wang, Yu Wang, Shuo Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665436786

This paper presents a novel deep reinforcement learning (DRL) method to solve the locomotion control problem of the biomimetic underwater vehicle (BUV) with hybrid propulsion, in order to meet the challenge of intractable multi-fins coordination and the complex hydrodynamic model. The system overview of the BUV, named RoboDact, with two flexible long fins and a double-joint fishtail as hybrid propulsion, is introduced. After that, the locomotion control problem is modeled as a Markov decision process (MDP) to be solved. Therefore, the locomotion control method based on soft actor-critic (SAC, a novel DRL algorithm) is proposed. The simulation environment is established based on the kinetic model for interaction. Finally, the feasibility and effectiveness of the proposed control method is demonstrated after extensive simulations. It will provide rich insights into the coordination control of biomimetic underwater vehicles. © 2021 IEEE.

关键词： Reinforcement learning

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Economic Equilibrium with Random Transactions

Economic Equilibrium with Random Transactions

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International Conference on control systems, Mathematical modelling, Automation and Energy Efficiency (SUMMA)

作者： Aleksandra Zhukova Igor G. Pospelov Federal Research Center Computer Science and Control of RAS Moscow Institue of Physics and Technology Moscow Russia dept. of mathematical modelling of complex systems and optimization Moscow Russia

ISBN: (数字)9781728188409

ISBN: (纸本)9781728181134

This paper presents the advances in modelling the system of agents under the conditions of random transactions between them. We face certain challenges in this project which we would like to highlight in this presentation. The agents in the modelled system are a large number of consumers, a large number of producers and the bank as the intermediary. Consumers take loans from the bank to spend on consumption goods and producers borrow to make investment at random moments of time. The moments of time the agents deal form the Poisson flow. We present the approach to agents' optimal control problems by the Lagrange method instead of dynamic programming. This enables us obtaining the expressions for the optimal feedback control which appear to be linear in state variables. The solution to the optimal control problems are obtained using asymptotic methods assuming large frequency of transactions. Using the linearity of the agents' optimal control, we may aggregate description of agents, stock-flow consistent on the average. Instead of an ensemble of independent random transactions by agents, the description of the aggregate dynamics is deterministic. The system of equations for the aggregate dynamics is reduced to one equation. There are two cases of this equation, depending on the description of the bank. The equation has a specific form, its numerical analysis is challenging. We provide numerical results and discuss further directions of research.

关键词： Numerical analysis Aggregates Optimal control Process control Linearity Numerical models Mathematical model

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Real-Time Marker Localization Learning for GelStereo Tactile...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Liu, Shujuan Cui, Shaowei Zhang, Chaofan Cai, Yinghao Wang, Shuo University of Science and Technology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Institute of Automation Beijing100190 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better realtime performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks. © 2022 IEEE.

关键词： Chemical detection

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Color-sensitive person re-identification 28

Color-sensitive person re-identification

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28th International Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Wang, Guan'an Yang, Yang Y. Cheng, Jian Wang, Jinqiao Hou, Zengguang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9780999241141

Recent deep Re-ID models mainly focus on learning high-level semantic features, while failing to explicitly explore color information which is one of the most important cues for person Re-ID model. In this paper, we propose a novel Color-Sensitive Re-ID to take full advantage of color information. On one hand, we train our model with real and fake images. By using the extra fake images, more color information can be exploited and it can avoid over-fitting during training. On the other hand, we also train our model with images of the same person with different colors. By doing so, features can be forced to focus on the color difference in regions. To generate fake images with specified colors, we propose a novel Color Translation GAN (CTGAN) to learn mappings between different clothing colors and preserve identity consistency among the same person with the clothing color. Extensive evaluations on two benchmark datasets show that our approach significantly outperforms state-of-the-art Re-ID models. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

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