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Rail surface defect data enhancement method based on improved ACGAN Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-09 He Zhendong, Gao Xiangyang, Liu Zhiyuan, An Xiaoyu, Zheng Anping
Rail surface defects present a significant safety concern in railway operations. However, the scarcity of data poses challenges for employing deep learning in defect detection. This study proposes an enhanced ACGAN augmentation method to address these issues. Residual blocks mitigate vanishing gradient problems, while a spectral norm regularization-constrained discriminator improves stability and image
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Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-05
The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learning algorithms with autonomous intelligent systems, aiming to gain a better understanding of the
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Optimization method for human-robot command combinations of hexapod robot based on multi-objective constraints Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-05
Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting
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3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-05
In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking
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The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-05
IntroductionService robot technology is increasingly gaining prominence in the field of artificial intelligence. However, persistent limitations continue to impede its widespread implementation. In this regard, human motion pose estimation emerges as a crucial challenge necessary for enhancing the perceptual and decision-making capacities of service robots.MethodThis paper introduces a groundbreaking
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The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-05 XianFeng Tang, Shuwei Zhao
IntroductionService robot technology is increasingly gaining prominence in the field of artificial intelligence. However, persistent limitations continue to impede its widespread implementation. In this regard, human motion pose estimation emerges as a crucial challenge necessary for enhancing the perceptual and decision-making capacities of service robots.MethodThis paper introduces a groundbreaking
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3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-05 Xuefeng Wang, Yang Mi, Xiang Zhang
In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking
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Optimization method for human-robot command combinations of hexapod robot based on multi-objective constraints Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-05 Xiaolei Chen, Bo You, Zheng Dong
Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting
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Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-05 Dekang Zhu, Qianyi Bu, Zhongpan Zhu, Yujie Zhang, Zhipeng Wang
The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learning algorithms with autonomous intelligent systems, aiming to gain a better understanding of the
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Designing for usability: development and evaluation of a portable minimally-actuated haptic hand and forearm trainer for unsupervised stroke rehabilitation Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-04
In stroke rehabilitation, simple robotic devices hold the potential to increase the training dosage in group therapies and to enable continued therapy at home after hospital discharge. However, we identified a lack of portable and cost-effective devices that not only focus on improving motor functions but also address sensory deficits. Thus, we designed a minimally-actuated hand training device that
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Can lower-limb exoskeletons support sit-to-stand motions in frail elderly without crutches? A study combining optimal control and motion capture Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-04
With the global geriatric population expected to reach 1.5 billion by 2050, different assistive technologies have been developed to tackle age-associated movement impairments. Lower-limb robotic exoskeletons have the potential to support frail older adults while promoting activities of daily living, but the need for crutches may be challenging for this population. Crutches aid safety and stability
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Can lower-limb exoskeletons support sit-to-stand motions in frail elderly without crutches? A study combining optimal control and motion capture Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-04 Jan C. L. Lau, Katja Mombaur
With the global geriatric population expected to reach 1.5 billion by 2050, different assistive technologies have been developed to tackle age-associated movement impairments. Lower-limb robotic exoskeletons have the potential to support frail older adults while promoting activities of daily living, but the need for crutches may be challenging for this population. Crutches aid safety and stability
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Designing for usability: development and evaluation of a portable minimally-actuated haptic hand and forearm trainer for unsupervised stroke rehabilitation Front. Neurorobotics (IF 3.1) Pub Date : 2024-04-04 Raphael Rätz, Alexandre L. Ratschat, Nerea Cividanes-Garcia, Gerard M. Ribbers, Laura Marchal-Crespo
In stroke rehabilitation, simple robotic devices hold the potential to increase the training dosage in group therapies and to enable continued therapy at home after hospital discharge. However, we identified a lack of portable and cost-effective devices that not only focus on improving motor functions but also address sensory deficits. Thus, we designed a minimally-actuated hand training device that
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A reinforcement learning enhanced pseudo-inverse approach to self-collision avoidance of redundant robots Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-28 Tinghe Hong, Weibing Li, Kai Huang
IntroductionRedundant robots offer greater flexibility compared to non-redundant ones but are susceptible to increased collision risks when the end-effector approaches the robot's own links. Redundant degrees of freedom (DoFs) present an opportunity for collision avoidance; however, selecting an appropriate inverse kinematics (IK) solution remains challenging due to the infinite possible solutions
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Cardioid oscillator-based pattern generator for imitating the time-ratio-asymmetrical behavior of the lower limb exoskeleton Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-27 Qiang Fu, Tianhong Luo, TingQiong Cui, Xiangyu Ma, Shuang Liang, Yi Huang, Shengxue Wang
IntroductionPeriodicity, self-excitation, and time ratio asymmetry are the fundamental characteristics of the human gait. In order to imitate these mentioned characteristics, a pattern generator with four degrees of freedom is proposed based on cardioid oscillators developed by the authors.MethodThe proposed pattern generator is composed of four coupled cardioid oscillators, which are self-excited
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Brain-inspired semantic data augmentation for multi-style images Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-26 Wei Wang, Zhaowei Shang, Chengxing Li
Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When there is a large style difference between training data and testing data, common data
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Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-21 Johan Engström, Ran Wei, Anthony D. McDonald, Alfredo Garcia, Matthew O'Kelly, Leif Johnson
Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena
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A data-driven acceleration-level scheme for image-based visual servoing of manipulators with unknown structure Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-20 Liuyi Wen, Zhengtai Xie
The research on acceleration-level visual servoing of manipulators is crucial yet insufficient, which restricts the potential application range of visual servoing. To address this issue, this paper proposes a quadratic programming-based acceleration-level image-based visual servoing (AIVS) scheme, which considers joint constraints. Besides, aiming to address the unknown problems in visual servoing
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Deep reinforcement learning navigation via decision transformer in autonomous driving Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-19 Lun Ge, Xiaoguang Zhou, Yongqiang Li, Yongcong Wang
In real-world scenarios, making navigation decisions for autonomous driving involves a sequential set of steps. These judgments are made based on partial observations of the environment, while the underlying model of the environment remains unknown. A prevalent method for resolving such issues is reinforcement learning, in which the agent acquires knowledge through a succession of rewards in addition
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Human skill knowledge guided global trajectory policy reinforcement learning method Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-15 Yajing Zang, Pengfei Wang, Fusheng Zha, Wei Guo, Chuanfeng Li, Lining Sun
Traditional trajectory learning methods based on Imitation Learning (IL) only learn the existing trajectory knowledge from human demonstration. In this way, it can not adapt the trajectory knowledge to the task environment by interacting with the environment and fine-tuning the policy. To address this problem, a global trajectory learning method which combinines IL with Reinforcement Learning (RL)
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HiDeS: a higher-order-derivative-supervised neural ordinary differential equation for multi-robot systems and opinion dynamics Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-12 Meng Li, Wenyu Bian, Liangxiong Chen, Mei Liu
This paper addresses the limitations of current neural ordinary differential equations (NODEs) in modeling and predicting complex dynamics by introducing a novel framework called higher-order-derivative-supervised (HiDeS) NODE. This method extends traditional NODE frameworks by incorporating higher-order derivatives and their interactions into the modeling process, thereby enabling the capture of intricate
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Multimodal audio-visual robot fusing 3D CNN and CRNN for player behavior recognition and prediction in basketball matches Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-06 Haiyan Wang
IntroductionIntelligent robots play a crucial role in enhancing efficiency, reducing costs, and improving safety in the logistics industry. However, traditional path planning methods often struggle to adapt to dynamic environments, leading to issues such as collisions and conflicts. This study aims to address the challenges of path planning and control for logistics robots in complex environments.MethodsThe
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Assessment and analysis of accents in air traffic control speech: a fusion of deep learning and information theory Front. Neurorobotics (IF 3.1) Pub Date : 2024-03-05 Weijun Pan, Jian Zhang, Yumei Zhang, Peiyuan Jiang, Shuai Han
IntroductionEnhancing the generalization and reliability of speech recognition models in the field of air traffic control (ATC) is a challenging task. This is due to the limited storage, difficulty in acquisition, and high labeling costs of ATC speech data, which may result in data sample bias and class imbalance, leading to uncertainty and inaccuracy in speech recognition results. This study investigates
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Multi-channel high-order network representation learning research Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-29 Zhonglin Ye, Yanlong Tang, Haixing Zhao, Zhaoyang Wang, Ying Ji
The existing network representation learning algorithms mainly model the relationship between network nodes based on the structural features of the network, or use text features, hierarchical features and other external attributes to realize the network joint representation learning. Capturing global features of the network allows the obtained node vectors to retain more comprehensive feature information
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Deep learning-based control framework for dynamic contact processes in humanoid grasping Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-28 Shaowen Cheng, Yongbin Jin, Hongtao Wang
Humanoid grasping is a critical ability for anthropomorphic hand, and plays a significant role in the development of humanoid robots. In this article, we present a deep learning-based control framework for humanoid grasping, incorporating the dynamic contact process among the anthropomorphic hand, the object, and the environment. This method efficiently eliminates the constraints imposed by inaccessible
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SR-TTS: a rhyme-based end-to-end speech synthesis system Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-27 Yihao Yao, Tao Liang, Rui Feng, Keke Shi, Junxiao Yu, Wei Wang, Jianqing Li
Deep learning has significantly advanced text-to-speech (TTS) systems. These neural network-based systems have enhanced speech synthesis quality and are increasingly vital in applications like human-computer interaction. However, conventional TTS models still face challenges, as the synthesized speeches often lack naturalness and expressiveness. Additionally, the slow inference speed, reflecting low
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Motion planning framework based on dual-agent DDPG method for dual-arm robots guided by human joint angle constraints Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-22 Keyao Liang, Fusheng Zha, Wei Guo, Shengkai Liu, Pengfei Wang, Lining Sun
IntroductionReinforcement learning has been widely used in robot motion planning. However, for multi-step complex tasks of dual-arm robots, the trajectory planning method based on reinforcement learning still has some problems, such as ample exploration space, long training time, and uncontrollable training process. Based on the dual-agent depth deterministic strategy gradient (DADDPG) algorithm, this
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Metastability indexes global changes in the dynamic working point of the brain following brain stimulation Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-19 Rishabh Bapat, Anagh Pathak, Arpan Banerjee
Several studies have shown that coordination among neural ensembles is a key to understand human cognition. A well charted path is to identify coordination states associated with cognitive functions from spectral changes in the oscillations of EEG or MEG. A growing number of studies suggest that the tendency to switch between coordination states, sculpts the dynamic repertoire of the brain and can
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Keeping social distance in a classroom while interacting via a telepresence robot: a pilot study Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-14 Kristel Marmor, Janika Leoste, Mati Heidmets, Katrin Kangur, Martin Rebane, Jaanus Pöial, Tiina Kasuk
IntroductionThe use of various telecommunication tools has grown significantly. However, many of these tools (e.g., computer-based teleconferencing) are problematic in relaying non-verbal human communication. Telepresence robots (TPRs) are seen as telecommunication tools that can support non-verbal communication.MethodsIn this paper, we examine the usability of TPRs, and communication distance related
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Design and assessment of a reconfigurable behavioral assistive robot: a pilot study Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-14 Enming Shi, Wenzhuo Zhi, Wanxin Chen, Yuhang Han, Bi Zhang, Xingang Zhao
IntroductionFor patients with functional motor disorders of the lower limbs due to brain damage or accidental injury, restoring the ability to stand and walk plays an important role in clinical rehabilitation. Lower limb exoskeleton robots generally require patients to convert themselves to a standing position for use, while being a wearable device with limited movement distance.MethodsThis paper proposes
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Dynamic event-based optical identification and communication Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-12 Axel von Arnim, Jules Lecomte, Naima Elosegui Borras, Stanisław Woźniak, Angeliki Pantazi
Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range, and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras and, for tracking, sparse neuromorphic
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Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-12 Erich Mielke, Eric Townsend, David Wingate, John L. Salmon, Marc D. Killpack
Human teams are able to easily perform collaborative manipulation tasks. However, simultaneously manipulating a large extended object for a robot and human is a difficult task due to the inherent ambiguity in the desired motion. Our approach in this paper is to leverage data from human-human dyad experiments to determine motion intent for a physical human-robot co-manipulation task. We do this by showing
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ADAM: a robotic companion for enhanced quality of life in aging populations Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-09 Alicia Mora, Adrian Prados, Alberto Mendez, Gonzalo Espinoza, Pavel Gonzalez, Blanca Lopez, Victor Muñoz, Luis Moreno, Santiago Garrido, Ramon Barber
One of the major problems of today's society is the rapid aging of its population. Life expectancy is increasing, but the quality of life is not. Faced with the growing number of people who require cognitive or physical assistance, new technological tools are emerging to help them. In this article, we present the ADAM robot, a new robot designed for domestic physical assistance. It mainly consists
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Velocity-aware spatial-temporal attention LSTM model for inverse dynamic model learning of manipulators Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-09 Wenhui Huang, Yunhan Lin, Mingxin Liu, Huasong Min
IntroductionAn accurate inverse dynamics model of manipulators can be effectively learned using neural networks. However, further research is required to investigate the impact of spatiotemporal variations in manipulator motion sequences on network learning. In this work, the Velocity Aware Spatial-Temporal Attention Residual LSTM neural network (VA-STA-ResLSTM) is proposed to learn a more accurate
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Identifying the characteristics of patients with stroke who have difficulty benefiting from gait training with the hybrid assistive limb: a retrospective cohort study Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-08 Shingo Taki, Takeshi Imura, Tsubasa Mitsutake, Yuji Iwamoto, Ryo Tanaka, Naoki Imada, Hayato Araki, Osamu Araki
Robot-assisted gait training is effective for walking independence in stroke rehabilitation, the hybrid assistive limb (HAL) is an example. However, gait training with HAL may not be effective for everyone, and it is not clear who is not expected to benefit. Therefore, we aimed to identify the characteristics of stroke patients who have difficulty gaining benefits from gait training with HAL. We conducted
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YOLOv8-ACU: improved YOLOv8-pose for facial acupoint detection Front. Neurorobotics (IF 3.1) Pub Date : 2024-02-01 Zijian Yuan, Pengwei Shao, Jinran Li, Yinuo Wang, Zixuan Zhu, Weijie Qiu, Buqun Chen, Yan Tang, Aiqing Han
IntroductionAcupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy.MethodsThis study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances
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Enhancing hazardous material vehicle detection with advanced feature enhancement modules using HMV-YOLO Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-30 Ling Wang, Bushi Liu, Wei Shao, Zhe Li, Kailu Chang, Wenjie Zhu
The transportation of hazardous chemicals on roadways has raised significant safety concerns. Incidents involving these substances often lead to severe and devastating consequences. Consequently, there is a pressing need for real-time detection systems tailored for hazardous material vehicles. However, existing detection methods face challenges in accurately identifying smaller targets and achieving
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Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-30 Xinghe Xie, Liyan Chen, Shujia Qin, Fusheng Zha, Xinggang Fan
IntroductionAs an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities.MethodsThis technology assists
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Multimodal robotic music performance art based on GRU-GoogLeNet model fusing audiovisual perception Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-30 Lu Wang
The field of multimodal robotic musical performing arts has garnered significant interest due to its innovative potential. Conventional robots face limitations in understanding emotions and artistic expression in musical performances. Therefore, this paper explores the application of multimodal robots that integrate visual and auditory perception to enhance the quality and artistic expression in music
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A study on robot force control based on the GMM/GMR algorithm fusing different compensation strategies Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-29 Meng Xiao, Xuefei Zhang, Tie Zhang, Shouyan Chen, Yanbiao Zou, Wen Wu
To address traditional impedance control methods' difficulty with obtaining stable forces during robot-skin contact, a force control based on the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm fusing different compensation strategies is proposed. The contact relationship between a robot end effector and human skin is established through an impedance control model. To allow the
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Re-framing bio-plausible collision detection: identifying shared meta-properties through strategic prototyping Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-25 Haotian Wu, Shigang Yue, Cheng Hu
Insects exhibit remarkable abilities in navigating complex natural environments, whether it be evading predators, capturing prey, or seeking out con-specifics, all of which rely on their compact yet reliable neural systems. We explore the field of bio-inspired robotic vision systems, focusing on the locust inspired Lobula Giant Movement Detector (LGMD) models. The existing LGMD models are thoroughly
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Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-23 Lei Wang, Guangjun Liu
IntroductionIn the field of logistics warehousing robots, collaborative operation and coordinated control have always been challenging issues. Although deep learning and reinforcement learning methods have made some progress in solving these problems, however, current research still has shortcomings. In particular, research on adaptive sensing and real-time decision-making of multi-robot swarms has
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Multi-UAV simultaneous target assignment and path planning based on deep reinforcement learning in dynamic multiple obstacles environments Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-22 Xiaoran Kong, Yatong Zhou, Zhe Li, Shaohai Wang
Target assignment and path planning are crucial for the cooperativity of multiple unmanned aerial vehicles (UAV) systems. However, it is a challenge considering the dynamics of environments and the partial observability of UAVs. In this article, the problem of multi-UAV target assignment and path planning is formulated as a partially observable Markov decision process (POMDP), and a novel deep reinforcement
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Loop closure detection of visual SLAM based on variational autoencoder Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-19 Shibin Song, Fengjie Yu, Xiaojie Jiang, Jie Zhu, Weihao Cheng, Xiao Fang
Loop closure detection is an important module for simultaneous localization and mapping (SLAM). Correct detection of loops can reduce the cumulative drift in positioning. Because traditional detection methods rely on handicraft features, false positive detections can occur when the environment changes, resulting in incorrect estimates and an inability to obtain accurate maps. In this research paper
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Context-aware SAR image ship detection and recognition network Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-16 Chao Li, Chenke Yue, Hanfu Li, Zhile Wang
With the development of deep learning, synthetic aperture radar (SAR) ship detection and recognition based on deep learning have gained widespread application and advancement. However, there are still challenging issues, manifesting in two primary facets: firstly, the imaging mechanism of SAR results in significant noise interference, making it difficult to separate background noise from ship target
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ID-YOLOv7: an efficient method for insulator defect detection in power distribution network Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-15 Bojian Chen, Weihao Zhang, Wenbin Wu, Yiran Li, Zhuolei Chen, Chenglong Li
Insulators play a pivotal role in the reliability of power distribution networks, necessitating precise defect detection. However, compared with aerial insulator images of transmission network, insulator images of power distribution network contain more complex backgrounds and subtle insulator defects, it leads to high false detection rates and omission rates in current mainstream detection algorithms
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Target-aware transformer tracking with hard occlusion instance generation Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-10 Dingkun Xiao, Zhenzhong Wei, Guangjun Zhang
Visual tracking is a crucial task in computer vision that has been applied in diverse fields. Recently, transformer architecture has been widely applied in visual tracking and has become a mainstream framework instead of the Siamese structure. Although transformer-based trackers have demonstrated remarkable accuracy in general circumstances, their performance in occluded scenes remains unsatisfactory
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DILS: depth incremental learning strategy Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-08 Yanmei Wang, Zhi Han, Siquan Yu, Shaojie Zhang, Baichen Liu, Huijie Fan
There exist various methods for transferring knowledge between neural networks, such as parameter transfer, feature sharing, and knowledge distillation. However, these methods are typically applied when transferring knowledge between networks of equal size or from larger networks to smaller ones. Currently, there is a lack of methods for transferring knowledge from shallower networks to deeper ones
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Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-05 Ira H. Haraldsen, Christoffer Hatlestad-Hall, Camillo Marra, Hanna Renvall, Fernando Maestú, Jorge Acosta-Hernández, Soraya Alfonsin, Vebjørn Andersson, Abhilash Anand, Victor Ayllón, Aleksandar Babic, Asma Belhadi, Cindy Birck, Ricardo Bruña, Naike Caraglia, Claudia Carrarini, Erik Christensen, Americo Cicchetti, Signe Daugbjerg, Rossella Di Bidino, Ana Diaz-Ponce, Ainar Drews, Guido Maria Giuffrè
More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools
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Vehicle re-identification method based on multi-attribute dense linking network combined with distance control module Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-05 Xiaoming Sun, Yan Chen, Yan Duan, Yongliang Wang, Junkai Zhang, Bochao Su, Li Li
IntroductionVehicle re-identification is a crucial task in intelligent transportation systems, presenting enduring challenges. The primary challenge involves the inefficiency of vehicle re-identification, necessitating substantial time for recognition within extensive datasets. A secondary challenge arises from notable image variations of the same vehicle due to differing shooting angles, lighting
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Education robot object detection with a brain-inspired approach integrating Faster R-CNN, YOLOv3, and semi-supervised learning Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-04 Qing Hong, Hao Dong, Wei Deng, Yihan Ping
The development of education robots has brought tremendous potential and opportunities to the field of education. These intelligent machines can interact with students in classrooms and learning environments, providing personalized educational support. To enable education robots to fulfill their roles, they require accurate object detection capabilities to perceive and understand the surrounding environment
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A review of rigid point cloud registration based on deep learning Front. Neurorobotics (IF 3.1) Pub Date : 2024-01-04 Lei Chen, Changzhou Feng, Yunpeng Ma, Yikai Zhao, Chaorong Wang
With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning
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Weighted residual network for SAR automatic target recognition with data augmentation Front. Neurorobotics (IF 3.1) Pub Date : 2023-12-19 Junyu Li, Cheng Peng
IntroductionDecades of research have been dedicated to overcoming the obstacles inherent in synthetic aperture radar (SAR) automatic target recognition (ATR). The rise of deep learning technologies has brought a wave of new possibilities, demonstrating significant progress in the field. However, challenges like the susceptibility of SAR images to noise, the requirement for large-scale training datasets
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EEG-controlled tele-grasping for undefined objects Front. Neurorobotics (IF 3.1) Pub Date : 2023-12-19 Minki Kim, Myoung-Su Choi, Ga-Ram Jang, Ji-Hun Bae, Hyung-Soon Park
This paper presents a teleoperation system of robot grasping for undefined objects based on a real-time EEG (Electroencephalography) measurement and shared autonomy. When grasping an undefined object in an unstructured environment, real-time human decision is necessary since fully autonomous grasping may not handle uncertain situations. The proposed system allows involvement of a wide range of human
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Enhanced YOLOv7 integrated with small target enhancement for rapid detection of objects on water surfaces Front. Neurorobotics (IF 3.1) Pub Date : 2023-12-14 Jie Yu, Hao Zheng, Li Xie, Lei Zhang, Mei Yu, Jin Han
Unmanned surface vessel (USV) target detection algorithms often face challenges such as misdetection and omission of small targets due to significant variations in target scales and susceptibility to interference from complex environments. To address these issues, we propose a small target enhanced YOLOv7 (STE-YOLO) approach. Firstly, we introduce a specialized detection branch designed to identify
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Fusion of dual modalities of non-invasive sensory feedback for object profiling with prosthetic hands Front. Neurorobotics (IF 3.1) Pub Date : 2023-12-13 Jie Zhang, Chih-Hong Chou, Manzhao Hao, Yan Li, Yashuo Yu, Ning Lan
IntroductionEither non-invasive somatotopic or substitute sensory feedback is capable of conveying a single modality of sensory information from prosthetic hands to amputees. However, the neurocognitive ability of amputees to integrate multi-modality sensory information for functional discrimination is unclear. The purpose of this study was to assess the fusion of non-invasive somatotopic tactile and
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Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction Front. Neurorobotics (IF 3.1) Pub Date : 2023-12-12 Su Kyoung Kim, Elsa Andrea Kirchner
Robot learning based on implicitly extracted error detections (e.g., EEG-based error detections) has been well-investigated in human-robot interaction (HRI). In particular, the use of error-related potential (ErrP) evoked when recognizing errors is advantageous for robot learning when evaluation criteria cannot be explicitly defined, e.g., due to the complex behavior of robots. In most studies, erroneous
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Player detection method based on scale attention and scale equalization algorithm Front. Neurorobotics (IF 3.1) Pub Date : 2023-12-06 Pan Zhang, Jiangtao Luo
IntroductionObject detection methods for team ball games players often struggle due to their reliance on dataset scale statistics, resulting in missed detections for players with smaller bounding boxes and reduced accuracy for larger bounding boxes.MethodsThis study introduces a two-fold approach to address these challenges. Firstly, a novel multi-scale attention mechanism is proposed, aiming to reduce
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Mobip: a lightweight model for driving perception using MobileNet Front. Neurorobotics (IF 3.1) Pub Date : 2023-12-04 Minghui Ye, Jinhua Zhang
The visual perception model is critical to autonomous driving systems. It provides the information necessary for self-driving cars to make decisions in traffic scenes. We propose a lightweight multi-task network (Mobip) to simultaneously perform traffic object detection, drivable area segmentation, and lane line detection. The network consists of a shared encoder for feature extraction and two decoders
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Safe physical interaction with cobots: a multi-modal fusion approach for health monitoring Front. Neurorobotics (IF 3.1) Pub Date : 2023-12-04 Bo Guo, Huaming Liu, Lei Niu
Health monitoring is a critical aspect of personalized healthcare, enabling early detection, and intervention for various medical conditions. The emergence of cloud-based robot-assisted systems has opened new possibilities for efficient and remote health monitoring. In this paper, we present a Transformer-based Multi-modal Fusion approach for health monitoring, focusing on the effects of cognitive