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Online Model Predictive control of Robot Manipulator With Structured Deep Koopman Model

IEEE ROBOTICS AND automation LETTERS 2023年第5期8卷 3102-3109页

作者： Zhang, Jinxin Wang, Hongze Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The model predictive control (MPC) can provide the benefit of optimality (sub-optimality, exactly speaking) and explicitly treat hard constraints in both states and inputs, which makes it an attractive approach in the fields of robotics. However, the performance of this approach heavily depends on the system model and it is computationally intensive, which hinders its application in the real-time control of robotic systems with fast dynamics, such as the robotic manipulators. Data-driven modeling approaches based on the Koopman operator have the potential to remove the barriers to adopting the MPC in robotics, through learning a globally linear model. In this letter, we propose a novel Koopman model-the structured deep Koopman model, which can improve the accuracy of the learned linear model and reduce the number of states in the lifted space, through exploiting the deep Lipschitz neural network and making the lifted dynamics structured. We also prove the rationality of the presented method and provide a new perspective on Koopman operator-based models, which brings the local and global linearization methods under the same umbrella. The effectiveness of the presented method has been verified by simulations and a real-world robotic experiment.

关键词： Deep learning in robotics and automation model learning for control Koopman operator model predictive control Lipschitz neural network

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Distilling Siamese Trackers with Attention Mask 41

Distilling Siamese Trackers with Attention Mask

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第41届中国控制会议

作者： Han Sun Yongqiang Bai Wenbo Zhang State Key Lab of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In recent years,the introduction of Siamese network has brought new vitality to the object tracking ***,high-performance Siamese trackers cannot run at a real-time speed on mobile devices due to their complex and huge *** distillation is a common and effective model compression method,but it is difficult to be applied to the challenging task like object *** find out the fundamental cause is that the imbalance between the foreground and background in the object tracking task,which aggravates the problem of insufficient feature extraction ability of small ***,we propose the attention mask distillation(AMD) to help the student tracker focus on the foreground area faster and more *** attention mask can be easily obtained from the feature maps and brings fine-granularity to the traditional binary *** experimental results on OTB100 and VOT2018 show that our method enables the student tracker perform as well as the teacher *** the same time,it's able to run on the CPU at a hyper-real-time of 66 fps and achieves nearly 9 times model compression *** low computational and storage costs make it possible to deploy high-performance trackers on resource-constrained platforms.

关键词： Object Tracking Siamese Network Knowledge Distillation Attention Mask

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Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region 23

Enhancing Model-Based Step Adaptation for Push Recovery thro...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Egle, Tobias Yan, Yashuai Lee, Dongheui Ott, Christian Tu Wien Automation and Control Institute Vienna1040 Austria Tu Wien Autonomous Systems Lab Vienna1040 Austria German Aerospace Center Wessling Germany

ISBN: (纸本)9798350373578

This paper introduces a new approach to enhance the robustness of humanoid walking under strong perturbations, such as substantial pushes. Effective recovery from external disturbances requires bipedal robots to dynamically adjust their stepping strategies, including footstep positions and timing. Unlike most advanced walking controllers that restrict footstep locations to a predefined convex region, substantially limiting recoverable disturbances, our method leverages reinforcement learning to dynamically adjust the permissible footstep region, expanding it to a larger, effectively non-convex area and allowing cross-over stepping, which is crucial for counteracting large lateral pushes. Additionally, our method adapts footstep timing in real time to further extend the range of recoverable disturbances. Based on these adjustments, feasible footstep positions and DCM trajectory are planned by solving a QP. Finally, we employ a DCM controller and an inverse dynamics whole-body control framework to ensure the robot effectively follows the trajectory. © 2024 IEEE.

关键词： Reinforcement learning

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Algorithm for High-Dimensional Data Based on Comprehensible Deep Progressive Fuzzy Network 7

Algorithm for High-Dimensional Data Based on Comprehensible ...

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2023 7th International Symposium on Computer Science and Intelligent control, ISCSIC 2023

作者： Qian, Jinyang Qi, Ruiyun Nanjing University of Aeronautics and Astronautics State Key Lab. of Mechanics and Control for Aerospace Structures College of Automation Engineering Nanjing China

ISBN: (纸本)9798350342987

Aiming at the 'dimension explosion' challenge of fuzzy systems in high-dimensional data processing, this paper proposes an algorithm of DPFS (Deep progressive fuzzy system), whose structure is the connection of many low-dimensional fuzzy systems. Through the comparison of experiments with different data sets, it is found that: *** algorithm retains the advantages of strong interpretability of the fuzzy system. *** DPFS algorithm, characterized by its progressive structure, exhibits a superiority of 7.9% over machine learning algorithms like BP and RBF. Moreover, it outperforms fuzzy system algorithms such as DNFS by more than 31.5% in terms of accuracy on the BF dataset. The research demonstrates that DPFS serves as an effective fuzzy system inference algorithm capable of effectively handling high-dimensional data, showcasing robust comprehensive capabilities. © 2023 IEEE.

关键词： Fuzzy systems

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The Future of Management: DAO to Smart Organizations and Intelligent Operations

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第6期53卷 3389-3399页

作者： Li, Juanjuan Qin, Rui Wang, Fei-Yue Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Taipa Macao Peoples R China

In the future, management in smart societies will revolve around knowledge workers and the works they produce. This article is committed to explore new management framework, model, paradigm, and solution for organizing, managing, and measuring knowledge works. First, the parallel management framework is presented that would allow for the virtual-real interactions of humans in social space, robots in physical space, and digital humans in cyberspace to realize descriptive, predictive, and prescriptive intelligence for management. Then, the management foundation models are proposed by fusing scenarios engineering with artificial intelligence foundation models and cyber-physical-social systems. Moreover, the new management paradigm driven by decentralized autonomous organizations and operations is formulated for the advancement of smart organizations and intelligent operations. On these basis, the management operating systems that highlight features of simple intelligence, provable security, flexible scalability, and ecological harmony are finally put forward as new management solution.

关键词： Decentralized autonomous organizations and operations (DAO) management foundation model management intelligence management operating system (MOS) metaverse parallel management

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Robust LiDAR-Camera Alignment With Modality Adapted Local-to-Global Representation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第1期33卷 59-73页

作者： Zhu, Angfan Xiao, Yang Liu, Chengxin Cao, Zhiguo Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China

alignment (LCA) is an important preprocessing procedure for fusing LiDAR and camera data. For it, one key issue is to extract unified cross-modality representation for characterizing the heterogeneous LiDAR and camera data effectively and robustly. The main challenge is to resist the modality gap and visual data degradation during feature learning, while still maintaining strong representative power. To address this, a novel modality adapted local-to-global representation learning method is proposed. The research efforts are paid in 2 main folders via modality adaptation and capturing global spatial context. First for modality gap resistance, LiDAR and camera data is projected into the same depth map domain for unified representation learning. Particularly, LiDAR data is converted to depth map according to pre-acquired extrinsic parameters. Thanks to the recent advantage of deep learning based monocular depth estimation, camera data is transformed into depth map in data driven manner, which is jointly optimized with LCA. Secondly to capture global spatial context, ViT (vision transformer) is introduced to LCA. The concept of LCA token is proposed for aggregating the local spatial patterns to form global spatial representation with transformer encoding. And, it is shared by all the samples. In this way, it can involve global sample-level information to leverage generalization ability. The experiments on KITTI dataset verify superiority of our proposition. Furthermore, the proposed approach is more robust to camera data degeneration (e.g., imaging blurring and noise) often faced by the practical applications. Under some challenging test cases, the performance advancement of our method is over 1.9 cm/4.1(? )on translation / rotation error. While our model size (8.77M) is much smaller than existing methods (e.g., LCCNet of 66.75M). The source code will be released at https://***/Zaf233/RLCA upon acceptance.

关键词： Cameras Laser radar Feature extraction Representation learning Estimation Transformers Point cloud compression LiDAR-Camera alignment modality adaptation global context vision transformer

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Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-Based BCIs

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 446-455页

作者： Bian, Rui Wu, Huanyu Liu, Bin Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Beijing Fenghuo Wanjia Technol Co Ltd Beijing 100026 Peoples R China

Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject. We propose small data least-squares transformation (sd-LST) to solve this problem. Experiments on three publicly available SSVEP datasets demonstrated that sd-LST outperformed several classical or state-of-the-art approaches, with only about 10 calibration trials for 40-target SSVEP-based BCI spellers.

关键词： Calibration Electroencephalography Correlation Visualization Task analysis Time-frequency analysis Standards Brain-computer interface steady-state visual evoked potential transfer learning least-squares transformation

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Event denoising for dynamic vision sensor using residual graph neural network with density-based spatial clustering☆

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NEUROCOMPUTING 2025年 636卷

作者： Feng, Weibin Wang, Xiaoping Zhan, Xin Huang, Hongzhi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain inspired Intelligent Syst Wuhan 430074 Peoples R China

The bio-inspired emerging dynamic vision sensor (DVS), characterized by its exceptional high temporal resolution and immediate response, possesses an innate advantage in capturing rapidly changing scenes. Nevertheless, it is also susceptible to severe noise interference, especially in challenging conditions like low illumination and high exposure. Notably, the existing noise processing approaches tend to oversimplify data into 2-dimensional (2D) patterns, disregarding the sparse and irregular crucial event structure information that the DVS intrinsically provides via its asynchronous output. Aiming at these problems, we propose a residual graph neural network (RGNN) framework based on density spatial clustering for event denoising, called DBRGNN. Leveraging the temporal window rule, we extract non-overlapping event segments from the DVS event stream and adopt a density-based spatial clustering algorithm to obtain event groups with spatial correlations. To fully exploit the inherent sparsity and plentiful spatiotemporal information of the raw event stream, we transform each event group as compact graph representations via directed edges and feed them into a graph coding module composed of a series of graph convolutional and pooling layers to learn robust geometric features from event sequences. Importantly, our approach effectively reduces noise levels without compromising the spatial structure and temporal coherence of spike events. Compared with other baseline methods, our DBRGNN achieves competitive performance by quantitative and qualitative evaluations on publicly available datasets under varying lighting conditions and noise ratios.

关键词： DVS Event denoising Temporal window Density-based clustering Graph learning for visual processing

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Noise-Aware Sparse Gaussian Processes and Application to Reliable Industrial Machinery Health Monitoring

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第4期19卷 5995-6005页

作者： Yang, Jingyu Yue, Zuogong Yuan, Ye Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China

Maintenance of machinery equipment in smart manufacturing requires real-time health monitoring, strongly supported by the rapid evolution of Artificial Intelligence (AI) technologies. Most AI-based health monitoring systems are powered by advanced modeling methods and intensive high-quality monitoring data. Such monitoring systems center on high-accuracy predictive performance but cannot necessarily convey reliability, such as satisfactory resistance to strong noises, credible uncertainty analysis, and model interpretability. This article novelly proposes noise-aware sparse Gaussian processes (NASGP) within the Bayesian inference framework. NASGP are capable of consistent high-performance and credible uncertainty assessment under strong noises. Based on NASGP, we then develop an explainable generalized additive model to bridge the gap between latent inference mechanism and domain expert knowledge. The efficacy of the proposed approach is corroborated through two case studies including remaining useful life prognosis and fault diagnosis for rolling bearings.

关键词： Monitoring Uncertainty Training Reliability Machinery Gaussian processes Bayes methods Explainable artificial intelligence machinery health monitoring reliability sparse Gaussian processes

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