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AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models

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arXiv 2024年

作者： Yao, Yifei He, Wentao Gu, Chenyu Du, Jiaheng Tan, Fuwei Zhu, Zhen Lu, Junguo All authors are with Machine Vision and Autonomous System Laboratory Department of Automation School of Electrical Information and Electronic Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China

Training and deploying reinforcement learning (RL) policies for robots, especially in accomplishing specific tasks, presents substantial challenges. Recent advancements have explored diverse reward function designs, training techniques, simulation-to-reality (sim-to-real) transfers, and performance analysis methodologies, yet these still require significant human intervention. This paper introduces an end-to-end framework for training and deploying RL policies, guided by Large Language Models (LLMs), and evaluates its effectiveness on bipedal robots. The framework consists of three interconnected modules: an LLM-guided reward function design module, an RL training module leveraging prior work, and a sim-to-real homomorphic evaluation module. This design significantly reduces the need for human input by utilizing only essential simulation and deployment platforms, with the option to incorporate human-engineered strategies and historical data. We detail the construction of these modules, their advantages over traditional approaches, and demonstrate the framework’s capability to autonomously develop and refine controlling strategies for bipedal robot locomotion, showcasing its potential to operate independently of human intervention. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Unsupervised Time Series Anomaly Detection under Data Contamination 12

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Unsupervised Time Series Anomaly Detection under Data Contam...

12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Lin, Xiaohui Li, Zuoyong Huang, Xunhua Chen, Xinwei Fan, Haoyi Fuzhou University College of Computer and Data Science Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China Minjiang University Fujian Engineering and Research Center of New Chinese Lacquer Materials Fuzhou350121 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China

ISBN: (纸本)9798350310153

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE. © 2022 IEEE.

关键词： Contamination

Capacity Optimization of a Hybrid Energy Storage System Considering Wind-Solar Reliability Evaluation Based on a Novel Multi-Strategy Snake Optimization Algorithm

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SSRN

SSRN 2022年

作者： Wang, Chen Jiao, Shangbin Li, Yujun Zhang, Qing School of Automation and Information Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China

When the capacity configuration of a hybrid energy storage system (HESS) is optimized considering the reliability of a wind turbine and photovoltaic generator (PVG), the sequential Monte Carlo method is typically adopted to simulate the normal operation and fault probability of wind turbines and PVG units. The capacity optimization configuration model of the HESS is constructed with the life cost cycle (LCC) as an objective and the loss of power supply probability (LPSP) as constraints. The variational mode decomposition is used to decompose the unbalanced power in the wind-PV microgrid. The maximum power of the optimized the battery bank and the supercapacitor bank are used to determine the frequency division point, and the high-frequency power and low-frequency power are respectively allocated to the supercapacitor bank and the battery bank. When the snake optimizer (SO) is used to solve the nonconvex capacity optimization configuration model of the HESS, poor accuracy and stability are significant problems. The opposition-based learning, adaptive weight factor, and Lévy flight strategy are introduced in the algorithm framework. Twenty-three test functions show that multi strategy snake optimizer (MSO) significantly improves the accuracy and convergence speed compared with SO. Nine optimization algorithms including MSO are used to solve the capacity optimization problem of the HESS. The results show that MSO is superior to other algorithms in performance and numerical accuracy. It can reduce the LCC 4.5% and ensure that the capacity of the HESS meets the load demand when the wind turbine and PVGs are in a fault state. © 2022, The Authors. All rights reserved.

关键词： Variational mode decomposition

Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis

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arXiv 2024年

作者： Liu, Chenyang Chen, keyan Zhang, Haotian Qi, Zipeng Zou, Zhengxia Shi, Zhenwei The Image Processing Center School of Astronautics Beihang University Beijing100191 China The Beijing Key Laboratory of Digital Media Beihang University Beijing100191 China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shen Yuan Honors College of Beihang University Beijing100191 China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Monitoring changes in the Earth’s surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Remote sensing satellite imagery offers a unique perspective for monitoring these changes, leading to the emergence of remote sensing image change interpretation (RSICI) as a significant research focus. Current RSICI technology encompasses change detection and change captioning, each with its limitations in providing comprehensive interpretation. To address this, we propose an interactive Change-Agent, which can follow user instructions to achieve comprehensive change interpretation and insightful analysis, such as change detection and change captioning, change object counting, change cause analysis, etc. The Change-Agent integrates a multi-level change interpretation (MCI) model as the eyes and a large language model (LLM) as the brain. The MCI model contains two branches of pixel-level change detection and semantic-level change captioning, in which the BI-temporal Iterative Interaction (BI3) layer is proposed to enhance the model’s discriminative feature representation capabilities. To support the training of the MCI model, we build the LEVIR-MCI dataset with a large number of change masks and captions of changes. Experiments demonstrate the SOTA performance of the MCI model in achieving both change detection and change description simultaneously, and highlight the promising application value of our Change-Agent in facilitating comprehensive interpretation of surface changes, which opens up a new avenue for intelligent remote sensing applications. © 2024, CC BY.

关键词： Change detection

LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

Design of three-dimensional gesture recognition and motion tracking human-computer intelligent interaction system based on PAJ7620

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Design of three-dimensional gesture recognition and motion t...

2021 International Conference on Information Technology and intelligent control, CITIC 2021

作者： Wang, Jian-Liang Wei, Ye Li, Yang Meng, Yuan Li, Ming-Yu Xu, Jiang-Yan Jiao, Wan-Ming Ban, Lan Liu, Jun-Pei Zhang, Hong-Feng Zhang, Wei Wang, Li-Na Luo, Fu-Chen Wang, Yu-Duo Tianjin College University of Science and Technology Beijing Tianjin China Civil Aviation University of China Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin China Tianjin Normal University Tianjin Key Laboratory of Wireless Mobile Communication and Radio Energy Transmission Tianjin China Binhai College Nankai University Tianjin China Tianjin Tianshi College Tianjin China

With the increasing degree of informatization and intelligence in modern society, human-computer interaction technology has become one of the commonly used scientific and technological achievements. "Gesture" can be used as inputing image information, and make more intuitive and convenient for perception of user interface. To a certain extent, it can complete the intelligent interaction function between human and computer. The design is a human-computer intelligent interaction system for 3D gesture recognition and motion tracking based on gesture sensor PAJ7620. STM32 chip is used as the main control unit to recognize accurately, analyze and process different gestures collected by the gesture sensor, and control the OLED screen to display the gesture information. It can further drive the carrier to take corresponding actions and realize the intelligent human-computer interaction recognition of gestures. The experimental test results show that the recognition rate is lose to 100% when the distance is within 10cm. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Gesture recognition

Synchronization of a Fourth-order Hyperchaotic System by Intermittent control with Double Impulses

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Synchronization of a Fourth-order Hyperchaotic System by Int...

2022 International Conference on intelligent Systems and Computational Intelligence, ICISCI 2022

作者： Lu, Zequn Onasanya, B.O. Feng, Yuming Xie, Xianyang Tang, Ning Zhang, Wei Chongqing Three Gorges University School Of Computer Science And Engineering Chongqing404100 China University Of Ibadan Department Of Mathematics Ibadan Nigeria Chongqing Three Gorges University School Of Three Gorges Artificital Intelligence Chongqing404100 China Chongqing Three Gorges University School Of Electronical And Information Engineering Chongqing404100 China Chongqing Three Gorges University Key Laboratory Of Intelligent Information Processing And Control Chongqing404100 China Chongqing Three Gorges University Chongqing Engineering Research Center Of Internet Of Things And Intelligent Control Technology Chongqing404100 China

ISBN: (数字)9781665456197

ISBN: (纸本)9781665456197

In this paper, we describe a hyperchaotic system with time-delay. The process of the system is composed of a drive system and a response system, which is controlled by an intermittent mode. Double impulses are added to enhance the control, which is closer to the actual situation. According to Lyapunov stability theory, Linear Matrix Inequality (LMI), double impulses and intermittent control theory, and considering the approximation method of delay equation, an exponential synchronization condition of error system is given. The validity of the conclusion is realized by computer simulation. © 2022 IEEE.

关键词： Time delay

Vehicle detection based on point cloud intensity and distance clustering 5

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Vehicle detection based on point cloud intensity and distanc...

2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Wang, Zhao Wang, Xing Fang, Bin Yu, Kun Ma, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Multispectral Information Processing Technology Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

Sensor-based Multi-agent Coverage control with Spatial Separation in Unstructured Environments

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arXiv 2024年

作者： Wang, Xinyi Xu, Jiwen Gao, Chuanxiang Chen, Yizhou Zhang, Jihan Wang, Chenggang Ding, Yulong Chen, Ben M. Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information System), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots