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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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Event-Triggered Energy Optimization of Wireless Sensor Networks 19

Event-Triggered Energy Optimization of Wireless Sensor Netwo...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Liu, Lu Song, Ruizhuo Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665472432

Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste. © 2022 IEEE.

关键词： Energy utilization

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Completed attention convolutional neural network for MRI image segmentation

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High Technology Letters 2022年第3期28卷 247-251页

作者： ZHANG Zhong LV Shijie LIU Shuang XIAO Baihua Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal UniversityTianjin 300387P.R.China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China

Attention mechanism combined with convolutional neural network(CNN) achieves promising performance for magnetic resonance imaging(MRI) image segmentation,however these methods only learn attention weights from single scale,resulting in incomplete attention learning.A novel method named completed attention convolutional neural network(CACNN) is proposed for MRI image ***,the channel-wise attention block(CWAB) and the pixel-wise attention block(PWAB) are designed to learn attention weights from the aspects of channel and pixel *** a result,completed attention weights are obtained,which is beneficial to discriminative feature *** method is verified on two widely used datasets(HVSMR and MRBrainS),and the experimental results demonstrate that the proposed method achieves better results than the state-of-theart methods.

关键词： magnetic resonance imaging(MRI)image segmentation completed attention convolutional neural network(CACNN)

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Trajectory control of Multi-Axis Robotic Arms Based on Physics-Informed Neural Networks and Nonlinear Model Predictive control

Trajectory Control of Multi-Axis Robotic Arms Based on Physi...

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2024 China automation Congress, CAC 2024

作者： Wang, Yiqing Xia, Changqing Jin, Xi Xu, Chi Zhang, Yanzhu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350368604

Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represent advanced methods that integrate physical laws with data-driven approaches. Despite their outstanding performance in integrating theory and data, research on their application for controlling robotic arms in complex scenarios remains limited. This paper establishes a nonlinear dynamic model based on the physical relationships between the axes of robotic arms. In the absence of prior data, Sobol sequence random sampling is employed to generate data, which are subsequently trained using PINNs. Considering system noise, the Extended Kalman Filter (EKF) is utilized to predict the next state in noisy environments, and Nonlinear Model Predictive control (NMPC) is implemented to control the robotic arm for trajectory tracking, achieving real-time control. Simulation results demonstrate that the proposed Discrete Physics-Informed Predictive control (DPIPC) method exhibits smaller position and velocity errors with less fluctuation compared to the method in the reference, indicating superior control capabilities. © 2024 IEEE.

关键词： Extended Kalman filters

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PVTimesNet: A Hybrid Deep Learning Method for Enhancing Ultra-short-term PV Power Forecasting

PVTimesNet: A Hybrid Deep Learning Method for Enhancing Ultr...

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2024 China automation Congress, CAC 2024

作者： Wu, Ji Wang, Kai Ma, Wenwen Zhang, Jingxin Wang, Shibo China Electric Power Research Institute Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350368604

Accurate ultra-short-term photovoltaic (PV) power forecasting is crucial for the real-time scheduling of grid systems. However, the inherent variability of solar energy makes this task extremely challenging. To enhance PV power forecasting, this paper introduces a novel hybrid model named PVTimesNet, designed to harness the strengths of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for effective feature extraction. PVTimesNet consists of two parallel branches: the CNN1d branch, which captures correlations between multiple variables and adjacent time steps, and the LSTM branch, which learns the temporal dependencies within the PV power sequences. The features extracted by both branches are then concatenated and passed through a fully connected layer to generate multi-step PV power forecasts. Experimental results demonstrate that for forecast horizons of 1 to 4 hours, the proposed model significantly outperforms individual models and other CNN-LSTM hybrid structures. Additionally, it exhibits superior performance compared to related methods for longer forecast horizons. © 2024 IEEE.

关键词： Hybrid power

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Cooperative Filtering with Range Measurements: A Distributed Constrained Zonotopic Method 7th

Cooperative Filtering with Range Measurements: A Distribute...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Ding, Yu Cong, Yirui Wang, Xiangke Cheng, Long College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819733316

This article studies the distributed estimation problem of a multi-agent system with bounded absolute and relative range measurements. Parts of the agents are with high-accuracy absolute measurements, which are considered as anchors;the other agents utilize low-accuracy absolute and relative range measurements, each derives an uncertain range that contains its true state in a distributed manner. Different from previous studies, we design a distributed algorithm to handle the range measurements based on extended constrained zonotopes, which has low computational complexity and high precision. With our proposed algorithm, agents can derive their uncertain range sequentially along the chain topology, such that agents with low-accuracy sensors can benefit from the high-accuracy absolute measurements of anchors and improve the estimation performance. Simulation results indicate the effectiveness of our algorithm and verify our method can significantly improve the estimation accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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An Immersive Learning System with Multimodal Cognitive Processes Inference

An Immersive Learning System with Multimodal Cognitive Proce...

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2022 Chinese automation Congress, CAC 2022

作者： Bian, Siyu Liu, Xiwei Zhao, Hongxia Gong, Xiaoyan Jing, Sifeng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China

ISBN: (纸本)9781665465335

Using multimodal technology to infer learners' cognitive processes is an important means to support personalized learning. Previous cognitive process analysis is more about subjective reasoning, and less about combining physiological signals. With the rapid development of virtual reality technology, new requirements and more immersive schemes for learner's cognitive process inferring have been proposed. Based on this, this paper proposes an immersive learning system with multimodal cognitive processes inference, which is composed of macro, mesoscopic and micro environments. The system analyzes learner's actions, emotions and attention to infer learner's cognitive process through more comprehensive and objective multimodal data. The system also combines eye movement, brainwaves and other data, and proposes a multimodal data acquisition and analysis scheme using HMD-based VR. This paper also provides an immersive learning cognitive processes inference scheme for individual online learning, offline classroom learning scenarios. © 2022 IEEE.

关键词： Learning systems

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System Dynamics-Based Analysis on Factors Influencing Comprehensive Competence of Primary and Secondary School Students

System Dynamics-Based Analysis on Factors Influencing Compre...

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2022 Chinese automation Congress, CAC 2022

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Zhao, Hongxia Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9781665465335

With rapid progress of artificial intelligence (AI), it is urgent to promote educational reform to improve comprehensive competence level of primary and secondary school students (CCLPSSS). However, educational reform is a system engineering involving multiple and intertwined factors. In this research, a system dynamics-based model of education reform is developed. Then, based on this model, a series of simulation experiments are carried out to find main factors affecting improvement of CCLPSSS. Results show that: 1) this model could simulate trends of CCLPSSS under different educational reform measures;2) Measures including teaching mode reform, development and application of AI-based new teaching method, implementation of Double Reduction in compulsory education, implementation of deepening education evaluation reform policy, and development and application of AI-based education evaluation technology could play significant roles in promoting CCLPSSS and further strengthening cultivation of AI compound talents. © 2022 IEEE.

关键词： Students

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Research on intelligent methods in virtual reality games

Research on intelligent methods in virtual reality games

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2022 Chinese automation Congress, CAC 2022

作者： Wang, JiaWei Liu, Xiwei Gong, Xiaoyan Chinese Academy of Sciences The State Key Laboratory for Management Control of Complex Systems Institute of Automation Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9781665465335

Introduction of virtual reality technology enriches the expression of traditional games and greatly improves the sense of substitution and interactive experience of users. Realization of virtual reality is inseparable from artificial intelligence technology. In order to improve immersion and interactivity of virtual reality games, it is necessary to propose more intelligent solutions to existing technologies. This paper aims to discuss some intelligent methods of virtual reality games. Firstly, it discusses artificial intelligence means to enhance the virtual reality experience, which can improve the tracking and force / tactile interaction system of existing virtual reality games from software and hardware aspects;Then it proposes the concept of human-machine hybrid enhanced intelligence: by introducing human cognitive model to improve perception and decision-making ability of virtual reality games, so as to make interaction of virtual reality games more friendly and more real. © 2022 IEEE.

关键词： Virtual reality

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Enhancing Robot Learning Through Cognitive Reasoning Trajectory Optimization Under Unknown Dynamics

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IEEE Robotics and automation Letters 2025年第6期10卷 5401-5408页

作者： Dong, Qingwei Wu, Tingting Zeng, Peng Zang, Chuanzhi Wan, Guangxi Cui, Shijie Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China China Mobile Research Institute Beijing100053 China Shenyang University of Technology School of Artifcial Intelligence Shenyang110870 China

In the domain of robot learning, equipping robots with the capability to swiftly acquire operational skills poses a significant challenge. Currently, reinforcement learning techniques are adept at addressing dynamic, unstructured problems involving rich contact scenarios. However, the convergence rate of these algorithms is often slow due to the high dimensionality of the robot state-action mapping space and the extensive initial policy search space. Meanwhile, advancements in large language models (LLMs) have endowed these models with a degree of logical reasoning ability, enabling them to take goal-oriented actions proactively during the initial phase of a robotic task. These models can implicitly generate features of states and uncover underlying patterns in trajectory generation. Yet, in complex manipulative tasks involving rich contact scenarios, LLMs still fall short. Thus, integrating the robust interactive capabilities of reinforcement learning with the strong logical reasoning of LLMs, and enhancing policy search with LLMs, could potentially accelerate the speed of policy searches significantly. In this letter, we introduce a Cognitive Reasoning Trajectory Optimization method. This approach utilizes Low-level Cognitive control Tuning to enable LLMs with robust logical reasoning to make effective single-step decisions in Markov Decision Process (MDP) tasks. By fitting dynamic models with high-quality cognitive reasoning data and optimizing control strategies, this method constrains the policy search space and enhances the efficiency of trajectory optimization. Experimental results on various manipulative tasks using the Sawyer robot in the Mujoco simulator validate the effectiveness of the proposed algorithm. © 2016 IEEE.

关键词： Markov processes

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