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arXiv 2024年

作者： Wang, Ze Li, Yang Xu, Long Shi, Hao Ma, Zunwang Chu, Zhen Li, Chao Gao, Fei Yang, Kailun Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University China School of Robotics and National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China State Key Laboratory of Industrial Control Technology Zhejiang University China DeepRobotics Co. Ltd. China

Dynamic jumping on high platforms and over gaps differentiates legged robots from wheeled counterparts. Compared to walking on rough terrains, dynamic locomotion on abrupt surfaces requires fusing proprioceptive and exteroceptive perception for explosive movements. In this paper, we propose SF-TIM (Simple Framework combining Terrain Imagination and Measurement), a single-policy method that enhances quadrupedal robot jumping agility, while preserving their fundamental blind walking capabilities. In addition, we introduce a terrain-guided reward design specifically to assist quadrupedal robots in high jumping, improving their performance in this task. To narrow the simulation-to-reality gap in quadrupedal robot learning, we introduce a stable and high-speed elevation map generation framework, enabling zero-shot simulation-to-reality transfer of locomotion ability. Our algorithm has been deployed and validated on both the small-/large-size quadrupedal robots, demonstrating its effectiveness in real-world applications: the robot has successfully traversed various high platforms and gaps, showing the robustness of our proposed approach. A demo video has been made available at https://***/SF-TIM. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning

arXiv

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arXiv 2025年

作者： Guo, Jiaxin Chen, C.L. Philip Li, Shuzhen Zhang, Tong Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for manual annotation. It provides high-quality data at a low annotation cost for label-scarce text classification. However, existing CSAL methods overlook weak classes and hard representative examples, resulting in biased learning. To address these issues, this paper proposes a novel dual-diversity enhancing and uncertainty-aware (DEUCE) framework for CSAL. Specifically, DEUCE leverages a pre-trained language model (PLM) to efficiently extract textual representations, class predictions, and predictive uncertainty. Then, it constructs a Dual-Neighbor Graph (DNG) to combine information on both textual diversity and class diversity, ensuring a balanced data distribution. It further propagates uncertainty information via density-based clustering to select hard representative instances. DEUCE performs well in selecting class-balanced and hard representative data by dual-diversity and informativeness. Experiments on six NLP datasets demonstrate the superiority and efficiency of DEUCE. © 2025, CC BY.

关键词： Active learning

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Temporal Group Attention Network with Affective Complementary Learning for Gait Emotion Recognition

Temporal Group Attention Network with Affective Complementar...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziyi Zhu C. L. Philip Chen Haiqi Liu Tong Zhang School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Pazhou Lab Guangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Skeleton-based methods in Gait Emotion Recognition (GER) that abstract gait as a spatial-temporal graph in a non-Euclidean space have achieved remarkable success. However, existing studies neglect capturing crucial spatial-temporal implicit dependencies and make insufficient use of manually crafted affective complementary information. In this paper, we propose a novel Temporal Group Attention Network with Affective Complementary Learning (T2A). Specifically, we propose a Temporal Group Attention (TGA) module, which captures spatial-temporal implicit dependencies and crucial features across space-time. Moreover, we design an affective complementary learning strategy, which first introduces an Affective Denoising (AD) module to reduce the impact of noise in weak emotion-related information and learn augmented affective features. Then, we adopt a decision-level fusion of the denoised affective features with the deep skeleton features to compensate for the discrepancies between different feature spaces. Experimental results on Emotion-Gait and EMOGAIT demonstrate that our proposed method significantly outperforms state-of-the-art methods.

关键词： Emotion recognition Noise reduction Noise Skeleton Bioinformatics

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Configuration-Aware robotic Trajectory Planning Based on Deep Deterministic Policy Gradients for Active Object Tracking

Configuration-Aware Robotic Trajectory Planning Based on Dee...

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IEEE International Conference on Mechatronics and Automation

作者： Yurou Zhang Zhenzhou Shao Shijie Guo College of Information Engineering and Beijing Key Laboratory of Light Industrial Robot and Safety Verification Capital Normal University Haidian District Beijing China Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Active object tracking provides a solution simultaneously addressing tracking and camera control, which can be usually implemented by robotic manipulators amounted with the camera at the end effector (e.g., move left, move forward, etc.). Trajectory planning is particularly the key technique for the control of robots during the tracking process. However, traditional trajectory planning methods are unable to achieve active tracking using robotic manipulators with different configurations, leading to poor generality and long training times. To address these problems, this paper proposes an improved trajectory planning method based on deep deterministic policy gradients (DDPG), to implement active object tracking tasks. A plug-in is designed to comply with the configuration of robotic manipulator. To improve generality and training efficiency, a novel reward function is proposed. Several experiments are conducted using a two-DoF robotic manipulator and a UR5 robot in the simulation and the physical two-DoF pantilt-zoom (PTZ). The proposed method can be applied to robots with different configurations. Experimental results demonstrate that the proposed method is feasible and has faster training speed and better active tracking performance compared with PPO, TD3, and SAC.

关键词： Training Accuracy Trajectory planning robot vision systems Process control Reinforcement learning Cameras

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Rechargeable Self-Powered Pressure Sensor Based on Zn-Ion Battery with High Sensitivity and Broad-Range Response

SSRN

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SSRN 2024年

作者： Hu, Yong-Peng Liu, Kai-Yang Bai, Ruo-Ning Liu, Da-Zhuang Yu, Wei Meng, Chui-Zhou Li, Guo-Xian Guo, Shi-Jie Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction Engineering Research Center The Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies School of Mechanical Engineering Hebei University of Technology No. 5340 Xiping Road Beichen District Tianjin300401 China

Flexible pressure sensors find broad applications in electronic skin, human-computer interaction, and health monitoring. However, developing self-powered flexible sensors capable of detecting both dynamic and static pressures with high sensitivity and a wide detection range remains a significant challenge. Here, we report the design and fabrication of a new rechargeable Zn-ion battery-type flexible self-powered pressure sensor (RZIB-FPS). In the device, the anode of a flexible Zn-ion battery is designed into a sandwich configuration with a porous isolation layer in between a conductive layer decorated with polymethylmethacrylate microspheres and a flexible Zn electrode. The self-powered pressure sensing mechanism relies on the variation in output voltage resulting from the resistance change of the anode when subjected to external pressure. This RZIB-FPS exhibits an open-circuit voltage of 1.46 V, an energy density of 392 µWh cm−2, a high sensitivity (up to 42.6 mV kPa−1), a wide pressure detection range (up to 330 kPa), and excellent cycling stability (up to 12,000 cycles). Furthermore, the proposed all-in-one device design demonstrates excellent charge-discharge performance, manifesting its high integration level and potential for sensor miniaturization. This study introduces a new strategy for developing high-performance sensors for future self-powered smart wearable electronics. © 2024, The Authors. All rights reserved.

关键词： Pressure sensors

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Wavelet Multi-level Multi-scale Neural Network for EEG Classification

Wavelet Multi-level Multi-scale Neural Network for EEG Class...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenghao Zhou Yisu Wang Fengming Zhao Ziyu Jia Chinese Academy of Sciences Beijing China Technology and Engineering Center for Space Utilization Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei China Key Laboratory of Biomedical Engineering of Hainan Province Haikou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Electroencephalogram (EEG) plays a crucial role in Brain-computer interface (BCI) research by recording brain electrical activity, allowing direct communication with external devices without relying on muscles or peripheral nerves. EEG signals are non-invasive and low-cost, which are commonly used in BCIs. Although existing work has achieved success in many BCI paradigms, there is still a significant challenge: how to automatically extract spectral features from raw EEG signals. To address this, we propose a novel wavelet multi-level multi-scale neural network for EEG-based BCIs, called wavelet-MMNN. Our model uses wavelet decomposition with multi-scale convolutional kernels to automatically extract abundant spectral features from the raw EEG signals. The cross-subject experiments and cross-session experiments are conducted on SSVEP Benchmark dataset, and the results indicate that the proposed model is superior to the state-of-the-art models.

关键词： Convolution Biological system modeling Benchmark testing Muscles Feature extraction Brain modeling Electroencephalography Brain-computer interfaces Recording Biological neural networks

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Restoration of FMRP expression in adult V1 neurons rescues visual deficits in a mouse model of fragile X syndrome

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Protein & Cell 2022年第3期13卷 203-219页

作者： Chaojuan Yang Yonglu Tian Feng Su Yangzhen Wang Mengna Liu Hongyi Wang Yaxuan Cui Peijiang Yuan Xiangning Li Anan Li Hui Gong Qingming Luo Desheng Zhu Peng Cao Yunbo Liu Xunli Wang Min-hua Luo Fuqiang Xu Wei Xiong Liecheng Wang Xiang-yao Li Chen Zhang State Key Laboratory of Membrane Biology PKU-IDG/McGovern Institute for Brain ResearchSchool of Life SciencesPeking UniversityBeijing 100871China School of Basic Medical Sciences Beijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing 100069China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China School of Life Sciences Tsinghua UniversityBeijing 100084China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Brain and Cognitive Sciences Institute of BiophysicsChinese Academy of Sciences(CAS)Beijing 100101China Institute of Laboratory Animal Science Peking Union Medical College/Chinese Academy of Medical ScienceBeijing 100021China Laboratory Animal Center Fujian University of Tradition Chinese MedicineFuzhou 350122China State Key Laboratory of Virology CAS Center for Excellence in Brain Science andIntelligence TechnologyWuhan Institute of VirologyCASWuhan 430071China Center for Brain Science Key Laboratory of Magnetic Resonance in Biological Systems and State Key Laboratory of Magnetic Resonance and Atomic and Molecular PhysicsWuhan Institute of Physics and MathematicsCASCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesWuhan 430071China Department of Physiology Anhui Medical UniversityHefei 230032China Department of Physiology Institute of Neuroscience and Collaborative Innovation Center for Brain ScienceSchool of MedicineZhejiang UniversityHangzhou 310058China

Many people affected by fragile X syndrome(FXS)and autism spectrum disorders have sensory processing deficits,such as hypersensitivity to auditory,tactile,and visual *** FXS in humans,loss of Fmr1 in rodents also cause sensory,behavioral,and cognitive ***,the neural mechanisms underlying sensory impairment,especially vision impairment,remain *** remains elusive whether the visual processing deficits originate from corrupted inputs,impaired perception in the primary sensory cortex,or altered integration in the higher cortex,and there is no effective *** this study,we used a genetic knockout mouse model(Fmr1^(KO)),in vivo imaging,and behavioral measurements to show that the loss of Fmr1 impaired signal processing in the primary visual cortex(V1).Specifically,Fmr1^(KO) mice showed enhanced responses to low-intensity stimuli but normal responses to high-intensity *** abnormality was accompanied by enhancements in local network connectivity in V1 microcircuits and increased dendritic complexity of V1 *** effects were ameliorated by the acute application of GABAA receptor activators,which enhanced the activity of inhibitory neurons,or by reintroducing Fmr1 gene expression in knockout V1 neurons in both juvenile and young-adult ***,V1 plays an important role in the visual abnormalities of Fmr1^(KO) mice and it could be possible to rescue the sensory disturbances in developed FXS and autism patients.

关键词： autism spectrum disorder calcium imaging fragile X syndrome primary visual cortex visual hypersensitivity

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USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER

arXiv

引用

arXiv 2023年

作者： Ma, Jun-Yu Gu, Jia-Chen Qi, Jiajun Ling, Zhen-Hua Liu, Quan Zhao, Xiaoyi National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence iFLYTEK Research China Communication University of China China

This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). A method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a statistics-based approach to construct a gazetteer. Secondly, the representations of gazetteer networks and language models are adapted by minimizing the KL divergence between them at both the sentence-level and entity-level. Finally, these two networks are then integrated for supervised named entity recognition (NER) training. The proposed method is applied to XLM-R with a gazetteer built from Wikidata, and shows great generalization ability across different tracks. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on one track (Hindi) in this task. Copyright © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Reinforcement Learning-based Fault-Tolerant Control for Unmanned Aerial Vehicles

Reinforcement Learning-based Fault-Tolerant Control for Unma...

引用

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Guoqi Wang Xudong Wang Yang Li Ziwei Gao Zhaoke Ning National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha China School of Aeronautics and Astronautics Sichuan University Chengdu China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper focuses on the fault-tolerant control (FTC) problem for unmanned aerial vehicles (UAVs) subject to possible multiple actuator failures, which is a tough problem to solve with traditional FTC methods since they usually require accurate mathematical models. To address the limitation of traditional FTC methods, a model-free FTC approach is proposed based on reinforcement learning (RL). Subsequently, the proposed approach is applied to construct fault-tolerant controller for UAVs without any knowledge of the quadrotor dynamic information. Then, an end-to-end control policy that can tolerant actuator failures is obtained, which can map the state of the UAVs directly to the control commands of the four rotors after learning. Finally, the effectiveness of the proposed fault-tolerant approach is demonstrated by using the flexible modular quadrotor simulator.

关键词： Fault tolerance Actuators Trajectory tracking Fault tolerant systems Autonomous aerial vehicles Stability analysis Mathematical models

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Modular Adaptive Aerial Manipulation under Unknown Dynamic Coupling Forces

arXiv

引用

arXiv 2024年

作者： Yadav, Rishabh Dev Dantu, Swati Pan, Wei Sun, Sihao Roy, Spandan Baldi, Simone Department of Computer Science The Univerity of Manchester United Kingdom Multi-robot systems group Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Department of Cognitive Robotics Delft Univesrity of Technology Netherlands Robotics Research Center International Institute of Information Technology Hyderabad India School of Mathematics Southeast University Nanjing China

Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches. © 2024, CC BY.

关键词： Adaptive control systems

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