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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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MPC control Strategy for Autonomous Vehicles Driving in Roundabouts

MPC Control Strategy for Autonomous Vehicles Driving in Roun...

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Mediterranean Conference on control and Automation (MED)

作者： Zsó fia Farkas Andrá s Mihá ly Pé ter Gá spá r Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd Research Network (ELKH) Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

The appearance of autonomous vehicles (AVs) in transportation has increased the attention of the scientific community to develop modern solutions for the control design of AVs in different traffic scenarios. In this paper a control method is proposed for the coordination of autonomous vehicles in roundabout scenarios. For collision avoidance and minimization of traveling time, a Model Predictive control (MPC) with a centralized controller is introduced to calculate the traveling times of the vehicles. A presented algorithm determines velocity profiles for safety reasons and for the reduction of possible congestion. The operation of the proposed MPC method is tested and demonstrated in CarSim simulation environment.

关键词： Automation control design Transportation Prediction algorithms Minimization Safety Collision avoidance

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Research on Engraving Recognition Method of Nodular Cast Iron Pipe Bearing End Based on Improved YOLOX

Research on Engraving Recognition Method of Nodular Cast Iro...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Mingxuan Han Jun Wang Kecheng Zhou Jian Wang School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This study proposes a method for recognizing the engravings on the spigot ends of ductile iron pipes based on an improved YOLOX model. Ductile iron pipes, known for their excellent mechanical properties and corrosion resistance, are widely used in critical fields such as municipal water supply, drainage, and gas transportation. To ensure product quality and achieve traceability, identification information is typically engraved on the spigot ends of ductile iron pipes during production. However, traditional manual recognition methods are inefficient and prone to human error. To address this issue, this paper proposes an automated engraving recognition system based on an improved YOLOX model. By integrating techniques such as Depthwise Separable Convolutions, a multi-scale feature extraction network, and an adaptive attention mechanism, the YOLOX model structure is further optimized to enhance recognition accuracy and robustness in complex industrial environments. Experimental results demonstrate that the improved YOLOX model surpasses traditional models in both recognition accuracy and computational efficiency, proving its effectiveness and reliability for industrial production applications.

关键词： Water Adaptation models Attention mechanisms Accuracy Computational modeling Transportation Production Feature extraction Robustness Computational efficiency

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Prototype for the Model-Based Assets and Liabilities Management Support System

Prototype for the Model-Based Assets and Liabilities Managem...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Aleksandra Zhukova Anna Flerova Maxim Tarasenko Alexey Chernov Ruslan Gabbasov Department of Mathematical Modelling of Economic Systems Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia Department of Mathematical Modelling of Economic Systems FRC CSC RAS Moscow Russia Laboratory of Numerical Methods of Applied Structural Optimization MIPT Moscow Russia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper presents the prototype of the instrument that supports the control of assets and liabilities by a bank using the mathematical computations. The specifics of the approach lies in the application of the recent advances in the control theory to provide the strategic analysis for various planning horizons and scenarios of external and internal factors.

关键词： Instruments Computational modeling Prototypes Mathematical models Planning control theory Information technology

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LiDAR-based drone navigation with reinforcement learning

arXiv

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arXiv 2023年

作者： Miera, Pawel Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest. © 2023, CC BY.

关键词： Robot Operating System

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LiDAR-based drone navigation with reinforcement learning

TechRxiv

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TechRxiv 2023年

关键词： Drones

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Adapting Human Mesh Recovery with Vision-Language Feedback

arXiv

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arXiv 2025年

作者： Xu, Chongyang Huang, Buzhen Zhang, Chengfang Feng, Ziliang Wang, Yangang College of Computer Science Sichuan University Chengdu610065 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The School of Automation Southeast University Nanjing210096 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China

Human mesh recovery can be approached using either regression-based or optimization-based methods. Regression models achieve high pose accuracy but struggle with model-to-image alignment due to the lack of explicit 2D-3D correspondences. In contrast, optimization-based methods align 3D models to 2D observations but are prone to local minima and depth ambiguity. In this work, we leverage large vision-language models (VLMs) to generate interactive body part descriptions, which serve as implicit constraints to enhance 3D perception and limit the optimization space. Specifically, we formulate monocular human mesh recovery as a distribution adaptation task by integrating both 2D observations and language descriptions. To bridge the gap between text and 3D pose signals, we first train a text encoder and a pose VQ-VAE, aligning texts to body poses in a shared latent space using contrastive learning. Subsequently, we employ a diffusion-based framework to refine the initial parameters guided by gradients derived from both 2D observations and text descriptions. Finally, the model can produce poses with accurate 3D perception and image consistency. Experimental results on multiple benchmarks validate its effectiveness. The code will be made publicly available. © 2025, CC BY.

关键词： Mesh generation

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

引用

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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Rational Gaussian wavelets and corresponding model driven neural networks

arXiv

引用

arXiv 2025年

作者： Ámon, Attila Miklós Fenech, Kristian Kovács, Péter Dózsa, Tamás Eötvös Loránd University Faculty of Informatics Department of Numerical Analysis Hungary Eötvös Loránd University Faculty of Informatics Department of Artificial Intelligence Hungary HUN-REN Institute for Computer Science and Control Systems and Control Laboratory Hungary University of Győr Hungary

In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a biomedical application, namely, the detection of ventricular ectopic beats (VEBs) in real ECG *** Codes 65D15 © 2025, CC BY.

关键词： Electrocardiography

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