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        <title>Frontiers in Neurorobotics | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/neurorobotics</link>
        <description>RSS Feed for Frontiers in Neurorobotics | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-08T10:34:21.970+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1795490</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1795490</link>
        <title><![CDATA[A lightweight Chinese–English translation model integrating compressed BERT attention and phrase discard mechanism]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xin Qi</author><author>Hang Bao</author>
        <description><![CDATA[Chinese–English machine translation based on neural network model strictly adopts the sequential modeling method of encoder–decoder. However, this traditional method cannot make effective use of syntactic information and linguistic hierarchy information. Therefore, to integrate syntactic structure information into Chinese–English machine translation to improve its translation performance, this paper proposes a new Chinese–English machine translation method based on graph convolutional network and BERT (Bidirectional Encoder Representation from Transformers) knowledge enhancement. In this work, we present an enhanced approach to neural machine translation that integrates multiple techniques to improve translation quality. The multi-BERT context is first compressed and aligned into the semantic space of the translation model using learnable compression vectors. This alignment ensures that the rich contextual information from BERT is effectively utilized within our translation framework. At the end of source language, we employ a dual encoder to encode both the source sentence and its syntactic dependency tree, thereby capturing both lexical and structural information. To further enrich the source-side semantic representation, the compression vector is concatenated with the input vector of the encoder. Additionally, we introduce a phrase discard mechanism that randomly discards target phrases during training. This mechanism enhances the model’s robustness against mistranslated phrases, thereby reducing their impact on subsequent phrase translations. Experiments on NIST dataset demonstrate the effectiveness of our proposed lightweight Chinese–English translation method. Different from general-purpose large chatbot models (e.g., ChatGPT) with high computing costs, this model achieves 39.68 BLEU with low parameters, solving issues of low-resource scenarios and phrase mistranslation. It offers a novel lightweight paradigm for private-oriented translation chatbots, outperforming the baseline Transformer (35.75 BLEU) significantly.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1769924</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1769924</link>
        <title><![CDATA[HarmoAtt-IK: an adaptive multimodal feature fusion network for real-time neural inverse kinematics]]></title>
        <pubdate>2026-04-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xukun Liu</author><author>Fengjuan Xie</author><author>Yu Liu</author><author>Jiaqiu Song</author>
        <description><![CDATA[IntroductionRecent advances in neural networks have introduced a new paradigm for robotic inverse kinematics. However, existing methods remain limited by insufficient feature extraction and suboptimal integration of multi-source information, preventing them from achieving high accuracy, broad generalization, and real-time performance on robots with diverse and complex kinematic structures.MethodsIn this work, we propose HarmoAtt-IK, an adaptive multimodal neural inverse kinematics approach designed for real-time inference and zero-collection training. Built upon the CycleIK framework, the proposed method introduces a novel adaptive multimodal attention fusion mechanism (HarmoAtt) that dynamically integrates the complementary strengths of spatial, channel, and cross-dimensional attention. It employs a temperature-adaptive Softmax function coupled with a compact weight-generation network to perform multidimensional extraction and adaptive enhancement of input features. We further introduce a composite loss function integrating an improved Smooth-L1 loss, a sign-invariant quaternion loss, and a Shannon entropy regularizer to enhance training stability and overall accuracy. Leveraging forward differential kinematics, our method enables rapid, cross-platform deployment by generating training data solely from URDF models, eliminating the need for costly physical data collection and manual annotation.ResultsExperimental evaluations on five humanoid platforms exhibiting substantial kinematic diversity demonstrate that HarmoAtt-IK attains maximum reductions of 76.4% in terminal positional error and 55.1% in rotational error relative to the baseline, while consistently improving the model’s inference success rate across all tested platforms by up to 5.76 percentage points.DiscussionThese results indicate that the proposed HarmoAtt-IK significantly outperforms baseline methods in both accuracy and reliability across diverse kinematic structures, highlighting the effectiveness of the adaptive multimodal attention mechanism and composite loss design. This further supports its potential for scalable, real-time deployment on a wide range of robotic platforms.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1753927</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1753927</link>
        <title><![CDATA[Interpretable vision mamba for SAR images classification]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kaiming Zou</author><author>Ningbo He</author><author>Leijun Yao</author><author>Jialin Li</author><author>Yongfeng Wang</author>
        <description><![CDATA[Deep learning techniques have been successfully applied to object classification in Synthetic Aperture Radar (SAR) images, achieving remarkable performance. However, the current Transformer architecture still faces two limitations in SAR object classification: (1) Transformers occupies substantial GPU memory during training, which hinders large-scale model deployment and hyperparameter optimization. (2) the vectorization of image patches destroys spatial information among pixels, thus the represented features are semantic-agnostic. In this paper, we propose a lightweight and interpretable vision mamba (IVim) whose feature maps from deep Mamba blocks are visually understandable. IVim consists of two modules: Token Semantic Re-allocation (TSR) and Token Attention Selection (TSA). TSR re-allocates the semantics to feature maps by optimizing a novel loss function for an extra convolutional layer. TAS selects the most discriminative attention map by evaluating the quantity of semantics in attention matrices of different Mamba layer. Finally, we provide a coupling strategy to form a saliency heatmap to visually show the interpretability of IVim by merging feature map and attention map. Experimental results demonstrate that IVim can re-allocate semantics to deep features and achieve better classification performance compared to its counterparts by reducing 67.8% parameters.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1775834</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1775834</link>
        <title><![CDATA[Design an anthropomorphic dexterous hand for expressive piano performance]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yanhong Liang</author><author>Xianwei Liu</author><author>Yanyan Yuan</author><author>Chengkai Su</author><author>Yucheng Tao</author><author>Yongbin Jin</author><author>Hongtao Wang</author>
        <description><![CDATA[BackgroundExpressive piano performance poses extreme challenges for robotic manipulation, necessitating high-speed repetitive impacts, substantial force output, and coordinated multi-joint control under stringent dynamic constraints. However, existing robotic systems exhibit significant limitations in replicating human-level dexterity, as well as achievable motion speed and force output. This work presents a data-driven, bio-inspired dexterous robotic hand designed specifically for high-fidelity piano performance.MethodsWe first extract kinematic primitives and stable inter-joint coupling patterns from large-scale motion capture data of professional pianists. These human motion priors are directly embedded into the mechanical architecture through morphological coupling and actuator allocation. Actuator selection is further guided by empirically measured human peak velocities and force profiles from biomechanics literature, ensuring sufficient bandwidth for high-speed repetitive motion and adequate force transmission.ResultsExperimental results demonstrate that the proposed hand replicates human-like joint coordination, achieves peak joint velocities of 53.88 rad/s, and provides sufficient fingertip force for authentic piano interaction. As a demonstration of its capabilities, the hand successfully performs a Grade 7 piano piece, Croatian Rhapsody, illustrating its potential for expressive musical performance.ConclusionThis research establishes a principled pathway from human motion statistics to embodied robotic intelligence, providing a high-performance hardware foundation for autonomous musical performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1806605</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1806605</link>
        <title><![CDATA[ActionX: pre-training action experts with reinforcement learning for vision-language action models]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tinghao Yi</author><author>Quantao Yang</author><author>Enhong Chen</author>
        <description><![CDATA[Vision-Language Action (VLA) models have enabled language-driven robotic manipulation by integrating language instructions, visual perception, and action generation. However, existing VLA approaches heavily rely on large-scale human demonstration datasets, which leads to substantial data collection and training costs. To address this, we propose ActionX, a pretraining framework that learns Action eXperts using reinforcement learning while leveraging a frozen, pretrained Vision-Language Model (VLM) backbone. The pretrained action expert is then integrated with the vision-language backbone and fine-tuned end-to-end using a small amount of expert data to align perception, language, and action for downstream manipulation tasks. We evaluate ActionX on the LIBERO and Meta-World benchmarks as well as real-world robotic manipulation scenarios. Experimental results show that ActionX achieves +16% success rate compare to state-of-the-art VLA models trained with large-scale demonstrations, while requiring only less than 100 expert demonstrations for real robot tasks for whole training phase. This performance is achieved by establishing an optimized action expert model through reinforcement learning, which significantly enhances VLA training efficiency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1761767</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1761767</link>
        <title><![CDATA[Trainable movement control using spikes and muscle-twitch dynamics]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jordi Timmermans</author><author>Lambert Schomaker</author>
        <description><![CDATA[The biological sensorimotor system is a source of inspiration for the design of neuromorphic ballistic control systems. A large portion of sensorimotor-inspired research focuses on the sensory encoding and information processing stages of the system. However, research on broader task-performance systems, involving actuator control on the output side remains scarce. In this work, we develop and train a neuromuscular-inspired model to perform ballistic control. In the model, a spiking neural network's output spikes are used to generate twitch-like signals. These twitches are the basis for generating a continuous fluctuating output signal that is used to operate an actuator. We refer to the the used model as the Twitch Neural Network (TwNN). As a test case, the model is trained to control the paddle of an adapted version of the game of Pong. An adapted version of the Direct Feedback Alignment learning rule, specifically for integrate-and-fire neurons, is introduced. The new rule avoids the update-locking problem of backpropagation, allowing network weight updates in parallel. The model output consists of one group of agonist-innervating motor neurons, and one group of antagonist-innervating motor neurons. We find that it is possible to teach a neuromuscular-inspired system to control the paddle in the game of Pong with the adapted Direct Feedback Alignment learning rule. The best-performing baseline model achieved a hit rate of 96%. By applying logarithmic scaling to the output activity, a hit rate of 98% could be achieved. Finally, by replacing the neuromorphically unrealistic exact summation steps with leaky integrators in training, the range of good learning parameters became more narrow and clear. The best-performing model reaches a hit rate of 99%. Threshold analysis during training has shown that learning is robust to a variety of neuron thresholds. Noise analysis has shown that the system is robust to membrane potential noise during inference for uniform noise up to values in the order of around 0.1-1% of the neuron threshold value per time step.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1785114</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1785114</link>
        <title><![CDATA[Multimodal human action recognition and personalized sports health promotion: a deep learning framework integrating wearable sensor fusion]]></title>
        <pubdate>2026-04-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ying Xi</author><author>Taibin Huang</author><author>Zhiyu Yang</author>
        <description><![CDATA[IntroductionIn real-world sports scenarios, Human Action Recognition (HAR) is often hindered by data complexity, limited dynamic adaptability, and fragmented integration of physiological and kinematic information. To address these challenges, this study proposes a multimodal HAR framework for personalized sports health promotion by integrating wearable sensor streams with deep learning architectures.MethodsThe proposed system employs a robust sensing layer to capture 12-dimensional multimodal data and synchronize physiological indicators with behavioral signals in real time. A novel Transformer-GCN hybrid model was developed to extract complex spatiotemporal dependencies for accurate action recognition and dynamic state analysis. In addition, a reinforcement learning module was incorporated to generate adaptive exercise prescriptions based on user progress. The framework was deployed through a responsive interface for real-time intervention and evaluated in a 12-week randomized controlled trial.ResultsThe results demonstrated that the proposed framework achieved effective multimodal fusion and reliable action recognition in sports scenarios. After the 12-week intervention, participants in the intervention group showed a 20.1% increase in cardiorespiratory fitness (VO2 max), a 99.3% improvement in muscular endurance, and a sports injury rate maintained below 15%. These findings indicate that the framework can support accurate motion analysis and safe, personalized intervention.DiscussionThe proposed multimodal fusion architecture effectively bridges the gap between action recognition and personalized sports health intervention. By combining wearable sensing, hybrid deep learning, and reinforcement learning, the framework provides a practical solution for AI-driven motion analysis and adaptive health promotion in land sports scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1838975</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1838975</link>
        <title><![CDATA[Correction: Enhancing 3D semantic scene completion with refinement module]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Frontiers Production Office </author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1829525</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1829525</link>
        <title><![CDATA[Correction: Neurorobotics for automotive manufacturing industry in era of embodied intelligence: a mini review]]></title>
        <pubdate>2026-04-02T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Bangcheng Zhang</author><author>Qi Xia</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1787501</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1787501</link>
        <title><![CDATA[Constructing three-way classifier with interval granulation neighborhood rough sets based on uncertainty invariance]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yongqi Wang</author><author>Taihua Xu</author><author>Rong Huang</author><author>Zhuangzhuang Liu</author><author>Jie Yang</author>
        <description><![CDATA[Three-way decision with neighborhood rough sets (3WDNRS) is effective in handling uncertain problems involving continuous data through the adjustment of the neighborhood radius. However, it faces two main limitations. Firstly, 3WDNRS relies on individual neighborhood granules as inputs, which can impair both decision efficiency and model generalizability. Secondly, the thresholds used in 3WDNRS often require predefinition based on prior knowledge, making the method difficult to apply in situations where such knowledge is lacking. To address these problems, this study introduces interval granulation (IG) into 3WD to construct an effective three-way classifier. Firstly, an interval granulation method based on DBSCAN is proposed. Then, an interval granulation neighborhood rough sets (IGNRS) model is presented, combining IG with quality indicators. Based on the IGNRS, a three-way classifier called 3WD-IGNRS is proposed by considering the principle of minimum fuzzy loss. Finally, extensive comparative experiments are conducted with three state-of-the-art granular-ball (GB)-based classifiers and four classical machine learning classifiers on 12 public benchmark datasets. The results demonstrate that our models consistently outperform the compared methods, achieving an average accuracy improvement of 4.94% compared to the best-performing granular-ball classifier.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1649168</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1649168</link>
        <title><![CDATA[Robust federated learning for UAV object detection: a joint self-distillation and drift compensation approach]]></title>
        <pubdate>2026-04-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yu Hangsun</author><author>Changnan Jiang</author><author>Ziyuan Zhang</author><author>Heqing Ouyang</author><author>Pengpeng Chen</author>
        <description><![CDATA[The rapid advancement of unmanned aerial vehicles (UAVs) in disaster response and environmental monitoring has underscored the growing importance of real-time object detection within UAV swarm networks. However, the non-independent and identically distributed (non-IID) characteristics of data in UAV networks present significant challenges to model convergence and adaptability. To tackle these challenges, this study introduces a robust federated UAV object detection framework tailored for non-IID data distributions. The framework aims to enhance adaptability across clients, thereby improving both detection performance and convergence speed. Our approach includes a self-distillation mechanism that leverages personalized knowledge from local model historical states to guide current local training, striking a balance between specialization and adaptability. Additionally, we propose a drift compensation mechanism to synchronize local and global model updates, mitigating model drift. We conducted extensive experiments on the VisDrone2019-DET dataset, comparing our method to baseline models. Results demonstrate that our approach accelerates convergence speed by approximately 2.2 times and enhances detection performance by around 3%, offering an efficient and robust solution for UAV-based object detection under non-IID conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1807995</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1807995</link>
        <title><![CDATA[A brain-inspired ISMO-PNN framework for neurally-grounded bearing fault diagnosis]]></title>
        <pubdate>2026-03-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fan Zhang</author><author>Bangcheng Zhang</author>
        <description><![CDATA[IntroductionIn advanced robot systems, monitoring the health of key components such as bearings in the transmission system is crucial for achieving reliable autonomous operation. However, there are still challenges in accurately diagnosing bearing faults under dynamic and noisy conditions.MethodsTo address this issue, this paper propose a brain-inspired computational framework that integrates an Improved Spider Monkey Optimization algorithm with a Probabilistic Neural Network (ISMO-PNN) for neurally-grounded bearing fault diagnosis in robotic systems. The main content includes: (1) extracting a 22 dimensional mixed feature set from vibration signals, (2) using intelligent PCA strategy to reduce the dimensionality of features to three dimensions while retaining more than 80% of the discriminative information, and (3) using ISMO algorithm to automatically optimize the key smoothing parameters of PNN.ResultsOn the CWRU bearing dataset, the ISMO-PNN model has a fault classification accuracy of 97.14% and a macro-average F1 score of 97.32%, which is superior to other comparative models in the article. In addition, the minimum training and testing accuracy difference of the model is 0.72%, indicating strong generalization ability.DiscussionThis brain-inspired framework, synergizing a neurally-grounded probabilistic classifier with a bio-inspired swarm optimizer, forms a robust and efficient embedded health monitoring model, which can provide feasible solutions for the development of advanced robot systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1816779</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1816779</link>
        <title><![CDATA[Editorial: Multi-modal learning with large-scale models]]></title>
        <pubdate>2026-03-13T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Xianmin Wang</author><author>Jing Li</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1766109</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1766109</link>
        <title><![CDATA[The evolution of trends and technology in wearable sensors used to detect falls in people with neurodegenerative diseases: a systematic review]]></title>
        <pubdate>2026-03-13T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Yuanzheng Chen</author><author>Tinghuai Huang</author><author>Zijie Lin</author><author>Quan Zhou</author>
        <description><![CDATA[BackgroundNeurodegenerative diseases (NDs) are a significant threat to human health. Numerous research demonstrated that patients with NDs might present with decreased balance, which is responsible for an increased risk of falling. As an emerging technology, wearable devices can detect falls and prevent privacy breaches.ObjectiveTo access the evolution of trends and technology in wearable devices to detect falls among patients with NDs.MethodsWe screened PubMed and Web of Science (February 2023) to summarize the pathway of fall detection with any body-worn sensor. Included articles were required to be full-text and published in English. Documents were excluded if they; (1) only used wearable devices for fall cueing, (2) did not offer sufficient information for data extraction, (3) did not use patients with NDs, (4) only used non-wearable sensors or devices.ResultsThe review identified 89 articles at the end of the procedure for data extraction. A wide variety existed in participant sample size (1–131), sensor types, placement and algorithms. 97.75% of papers (n = 87) used patients with Parkinson’s disease as experimental subjects. 21.45% of studies attached devices on the ankle (n = 19), with a clear preference for using multiple types of sensors (58.43% of studies, n = 52). As the most commonly used inertial measurement unit (IMU), 21 articles utilized accelerometers and gyroscopes to assess falls. 39.33% of studies (n = 35) choose data set to verify the effectiveness of their algorithm. Machine learning algorithms have become prevalent since 2019, and the most commonly used algorithm was support vector machine (SVM) (n = 17).ConclusionThese results show that an increasing number of researchers examine the validation performance of their systems in non-real-time. The ankle was the preferred location among researchers, and there is a clear preference to use multiple types of sensors and machine learning algorithms to improve accuracy and immediacy. Future work should focus on other NDs instead of limiting to Parkinson’s disease and consider an adequately studied population. A consensus on walking tasks and accuracy measurements is urgently needed. Performing studies in a simulated free-living environment for a specified time frame is advisable, with continuous real-time monitoring and assessment.Systematic review registrationPROSPERO, identifier (CRD42023405952).]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1768219</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1768219</link>
        <title><![CDATA[Enhancing 3D semantic scene completion with refinement module]]></title>
        <pubdate>2026-03-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dunxing Zhang</author><author>Jiachen Lu</author><author>Han Yang</author><author>Lei Bao</author><author>Bo Song</author>
        <description><![CDATA[We propose ESSC-RM, a plug-and-play Enhancing framework for Semantic Scene Completion with a Refinement Module, which can be seamlessly integrated into existing semantic scene completion (SSC) models. ESSC-RM operates in two phases: a baseline SSC network first produces a coarse voxel prediction, which is subsequently refined by a 3D U-Net–based Prediction Noise-Aware Module (PNAM) and Voxel-level Local Geometry Module (VLGM) under multiscale supervision. Experiments on SemanticKITTI show that ESSC-RM consistently improves semantic prediction performance. When integrated into CGFormer and MonoScene, the mean IoU increases from 16.87 to 17.27% and from 11.08 to 11.51%, respectively. These results demonstrate that ESSC-RM serves as a general refinement framework applicable to a wide range of SSC models. Project page: https://github.com/LuckyMax0722/ESSC-RM and https://github.com/LuckyMax0722/VLGSSC.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1757795</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1757795</link>
        <title><![CDATA[SpikeAEC: a neuromodulation-based spiking controller for explore-exploit balancing in mobile robots]]></title>
        <pubdate>2026-03-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Canyang Liu</author><author>Yichen Liu</author><author>Yongqi Zhou</author><author>Buqin Su</author>
        <description><![CDATA[Balancing exploration and exploitation remains a fundamental challenge in reliable mobile robot control, as conventional policies often converge on suboptimal behaviors. Inspired by the brain's division of labor for adaptive control, we propose SpikeAEC, a fully spiking, neuromodulated Actor-Explorer-Critic architecture designed to address this dilemma online within a closed-loop system. SpikeAEC comprises three specialized subnetworks operating in parallel: the Actor, inspired by the basal ganglia, proposes exploitative actions; the Explorer, modeled after the ACC-GPe-STN pathway, generates adaptive exploratory actions gated by a vigilance signal modulated by the accumulated global temporal-difference (TD) error; and the Critic, based on the ventral striatum, computes the TD error. The final action is selected by a separate, TAN-based Arbitrator, which probabilistically chooses between the Actor's and Explorer's action proposals according to recent performance and the TD error. These subnetworks are coupled through a unified three-factor learning framework that uses the TD signal and phasic neuromodulators (acetylcholine and dopamine) from the Arbitrator to drive pathway-specific synaptic plasticity. This online plasticity enhances the quality of action proposals and accelerates policy refinement. In simulation, SpikeAEC outperforms leading brain-inspired methods by converging 24% faster, reducing trajectory length by 18%, and increasing cumulative reward by over 5% against the top-performing baseline, all while maintaining consistency with established neurophysiological principles.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1796043</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1796043</link>
        <title><![CDATA[Neurorobotics for automotive manufacturing industry in era of embodied intelligence: a mini review]]></title>
        <pubdate>2026-03-03T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Bangcheng Zhang</author><author>Qi Xia</author>
        <description><![CDATA[As automotive manufacturing advances toward the industrial 5.0 era, traditional rigid automation production models are transitioning toward the embodied intelligence paradigm. Confronted with mass customization, diverse products, and small-batch production, the environment of automotive manufacturing exhibits high dynamism and unstructured characteristics. Different from traditional industrial intelligence based on static, hard-coded logic, robots enhance their cognitive abilities through closed-loop interaction with dynamic environments, inspired by bionic neural mechanisms, this shift enables robots to perform flexible and reliable operations in complex production scenarios. This paper analyzes the core role and key technologies of neural intelligence algorithms in reshaping perception, decision, and execution of industrial robot, while providing a systematic review of industrial robot evolution within the automotive industry, and provides a reliable path for future development.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1698100</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1698100</link>
        <title><![CDATA[AMSA-Net: attention-based multi-scale feature aggregation network for single image dehazing]]></title>
        <pubdate>2026-02-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shanqin Wang</author><author>Mengjun Miao</author><author>Miao Zhang</author>
        <description><![CDATA[ProblemDeep learning technology promotes the development of single-image dehazing. However, many existing methods fail to fully consider the haze density and its spatial distribution, which limits the improvement of dehazing performance.Proposed solutionTo address this issue, we propose an attention-based multi-scale feature aggregation network (AMSA-Net) for single-image dehazing.MethodAMSA-Net is an encoding and decoding structure. Its encoder and decoder are composed of multi-scale hybrid attention feature aggregation module (MSHA-FAM). The module can perceive the haze density and spatial information in the haze image, which helps to improve the dehazing effect. MSHA-FAM is composed of two key components: the scale-aware coordinate residual module (SCRM) and multi-scale feature refinement residual module (MSFRRM). SCRM uses improved coordinate attention to effectively capture haze density and spatial characteristics, thus significantly improving dehazing effect. MSFRRM extracts semantic features through up-sampling and down-sampling, and uses improved pixel attention mechanism to enhance key features. In the overall MSHA-FAM pipeline, SCRM first learns the density and spatial distribution characteristics of haze, then refines it through MSFRRM, so as to remove haze more effectively.Key resultsThe experimental results demonstrate that our proposed AMSA-Net is superior to the comparison methods in terms of dehazing quality. Ablation studies further verify the effectiveness of the proposed modules.ImpactIn this work, we present AMSA-Net, which has achieved good dehazing performance and can provide high-quality input for subsequent computer vision tasks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2025.1678984</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2025.1678984</link>
        <title><![CDATA[Emotion estimation from video footage with LSTM]]></title>
        <pubdate>2026-02-06T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Samer Attrah</author>
        <description><![CDATA[Emotion estimation is a field that has been studied for a long time, and several approaches using machine learning models exist. This article presents BlendFER-Lite, an LSTM model that uses Blendshapes from the MediaPipe library to analyze facial expressions detected from a live-streamed camera feed. This model is trained on the FER2013 dataset and achieves 71% accuracy and an F1-score of 62%, meeting the accuracy benchmark for the FER2013 dataset while significantly reducing computational costs compared to current methods. For the sake of reproducibility, the code repository, datasets, and models proposed in this paper, in addition to the preprint, can be found on Hugging Face at: https://huggingface.co/papers/2501.13432.JEL ClassificationD8, H51MSC Classification35A01, 65L10, 65L12, 65L20, 65L70]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1760494</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1760494</link>
        <title><![CDATA[Multimodal sequence dynamics and convergence optimization in dual-stream LSTM networks for complex physiological state estimation]]></title>
        <pubdate>2026-02-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaoxiao Cao</author>
        <description><![CDATA[IntroductionThe integration of virtual simulation with intelligent modeling is crucial for advancing the scientization and personalization of volleyball physical training. This study aims to overcome the convergence instability and feature misalignment in modeling multimodal kinematic and physiological sequences.MethodsA dynamical framework based on a Dual-Stream Long Short-Term Memory network integrated with a temporal attention mechanism is proposed. The framework decouples heterogeneous feature learning and optimizes temporal weight distribution.ResultsExperimental validation on complex motion state estimation demonstrates that the proposed model reduces load modeling error to 3.8% and achieves a motion classification accuracy of 93.1%. The velocity trajectory fitting coefficient of determination is 0.91 with a peak deviation of 0.05 m/s.DiscussionThese results confirm the effectiveness of the attention-based DS-LSTM in optimizing multimodal sequence modeling for training state estimation and feedback.]]></description>
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