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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>2026-08-22T20:46:30.264+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1858496</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1858496</link>
        <title><![CDATA[A group brain-controlled method for UAVs using a hybrid paradigm of hand movements and visual evoked potentials]]></title>
        <pubdate>2026-08-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rui Li</author><author>Shikun Bian</author><author>Yaxi Deng</author><author>Xiaoqing Wei</author><author>Shiqiang Yang</author><author>Yang Liu</author><author>Jiangcheng Chen</author>
        <description><![CDATA[Brain–computer interfaces (BCIs) are among the most prominent communication technologies that establish a direct channel for information exchange between the brain and external devices. They have been extensively applied in the field of aerospace. However, traditional BCI technology faces challenges, including a limited number of brain control commands and insufficient recognition accuracy in electroencephalography (EEG) decoding. These limitations make it difficult for traditional BCIs to perform complex tasks with high accuracy. Therefore, this study proposed a novel group BCI (G-BCI) system and further constructed a brain–machine shared control method for unmanned aerial vehicle (UAV) swarm control. First, a novel G-BCI paradigm combining precise hand movements and visual evoked potentials was designed. Moreover, an improved multi-domain feature fusion convolutional neural network (MDFF-CNN) was employed to decode EEG and electromyography (EMG) signals from precise hand movements, while a Filter Bank Common Spatial Patterns with Canonical Correlation Analysis (FBCCA) method was used for Steady-State Visual Evoked Potentials (SSVEP) decoding. Furthermore, a task-driven shared control model mapping the G-BCI system and the leader–follower UAV swarm control strategy was proposed. To verify the effectiveness of the proposed method, eight participants were recruited to conduct both offline and online experiments. The proposed G-BCI system achieved an offline accuracy of 88.91 ± 5.06% and an online accuracy of 88.89 ± 1.96%. All the experimental results demonstrate the feasibility of the proposed method.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1882865</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1882865</link>
        <title><![CDATA[Two-layer hierarchical MPPT strategy of PV systems based on AF-EPO algorithm under complex irradiance conditions]]></title>
        <pubdate>2026-08-14T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Lei Wang</author><author>Jinzhao Cui</author><author>Chao Ge</author><author>Zhixin Lun</author>
        <description><![CDATA[Photovoltaic power generation systems exhibit multi-peak power–voltage characteristics under partial shading conditions, severely limiting the effectiveness of conventional maximum power point tracking methods. This paper proposes a two-layer hierarchical MPPT architecture based on an adaptive fuzzy-weighted eagle perching optimization algorithm, designated AF-EPO. Unlike existing fuzzy-metaheuristic MPPT hybrids that apply fuzzy logic as an exogenous regulator to secondary control variables while retaining fixed-parameter core dynamics, AF-EPO embeds the fuzzy inference system directly into the endogenous EPO scaling factor, jointly driven by iteration progress and population diversity, thereby addressing three persistent limitations of metaheuristic MPPT through structural changes absent from existing fuzzy-hybrid formulations: the fixed-parameter exploration–exploitation dilemma, the computational overhead of unconditional global search, and the residual steady-state power oscillation that persists in all population-based methods. A slope sign-reversal detection module first identifies whether the power–voltage curve is unimodal or multi-modal, activating the global search layer only when partial shading is confirmed. In the global layer, a 25-rule Mamdani fuzzy inference system dynamically adjusts the EPO scaling factor according to iteration progress and population diversity, balancing exploration and exploitation throughout the search. A variable-step incremental conductance controller then refines the operating point to suppress steady-state oscillation. Simulations across three scenarios demonstrate that AF-EPO reduces tracking time by 47.3%, power oscillation rate by 74.6%, and energy loss by 38.2% compared with standard EPO. Hardware experiments on a TMS320F28335 DSP platform confirm tracking efficiencies exceeding 98.1%, with a maximum simulation-to-experiment deviation of 1.1%, validating the practical effectiveness of the proposed method.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1912825</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1912825</link>
        <title><![CDATA[Intention-driven ankle exoskeleton training acutely alters swing foot clearance distribution: a preliminary study]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Hanatsu Nagano</author><author>Rezaul Begg</author><author>Eri Sarashina</author><author>Hiroaki Kawamoto</author>
        <description><![CDATA[IntroductionTripping is a leading cause of falls, and Minimum Foot Clearance (MFC)—the lowest vertical swing-foot displacement during mid-swing—is a key gait event associated with tripping risk. Preventing tripping requires not only sufficiently high MFC but also consistent control across steps, which depends on neuromotor rather than strength-based mechanisms. We hypothesized that aligning movement intention with repeated, optimal ankle motion using a surface electromyography (sEMG)-driven exoskeleton would improve neuromotor consistency and MFC characteristics.MethodsA total of 12 healthy adults completed 5 min of treadmill walking at 4 km/h before and after a 5-min ankle exoskeleton training sequence (‘feet-flat, toes-up, feet-flat, heels-up’, 60 bpm). The Vicon motion capture system tracked the reflective markers placed on the heel and toe to collect MFC dataset. The exoskeleton (i.e., Hybrid Assistive Limb) detects voluntary neural drive through sEMG and converts it into assisted ankle motion, supporting intention-based neuromotor training.ResultsThe obtained MFC dataset showed that training increased the lowest percentile of the MFC distribution (i.e., the risky tail) by 0.39 cm, reduced step-to-step variability, and increased dataset skewness, suggesting reduction of both low and high extremes in swing-foot clearance.DiscussionThese preliminary findings suggest that brief intention-based ankle exoskeleton training may acutely improve safety-relevant characteristics of MFC control. Further studies in older adults and neurological populations are required to determine clinical relevance for tripping-risk reduction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1907235</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1907235</link>
        <title><![CDATA[Correction: ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation]]></title>
        <pubdate>2026-08-03T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Xukun Liu</author><author>Fengjuan Xie</author><author>Kai Xu</author><author>Zhenyu Liu</author><author>Shenggang Wei</author><author>Guangning Li</author><author>Xu Sun</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1814977</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1814977</link>
        <title><![CDATA[Morphology-agnostic humanoid retargeting via perception-motivated graph similarity]]></title>
        <pubdate>2026-07-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chaojie Fu</author><author>Chengkai Su</author><author>Lei Jiang</author><author>Kaixin Lan</author><author>Yongbin Jin</author><author>Hongtao Wang</author>
        <description><![CDATA[Quantifying motion similarity, despite its inherently subjective nature, is a foundational problem for humanoid motion transfer and control across different embodiments. Drawing from cognitive studies revealing that human motion similarity judgments are strongly influenced by spatial relationships among body parts and proximal contacts, we develop a graph-based representation that effectively encapsulates both features. This representation enables the definition of a robust similarity metric through graph distance computations. The proposed metric emphasizes spatial, especially proximal, relationships between body parts, facilitating motion retargeting that preserves these perceptually motivated relational cues across humanoid embodiments with shared semantic body parts and varying Degree of Freedom (DoF) configurations and body proportions. For quantitative retargeting evaluation, we introduce an order-preserving spatial similarity metric that measures how consistently inter-joint distance rankings are preserved between source and target motions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1855550</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1855550</link>
        <title><![CDATA[AI-driven quadruped robots: from fundamental locomotion to advanced biomimetic behaviors]]></title>
        <pubdate>2026-07-23T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Likai Wu</author><author>Chee-Onn Chow</author><author>Wei Ru Wong</author><author>Joon Huang Chuah</author><author>Jeevan Kanesan</author><author>Meina Zhang</author>
        <description><![CDATA[Quadruped robots have attracted increasing attention because they can traverse uneven terrain, support field deployment, and perform tasks that are difficult for wheeled or tracked platforms. Recent advances in artificial intelligence (AI) have further expanded their capabilities from manually designed gait control toward learning-based locomotion, perception-aware adaptation, dynamic motion skills, autonomous recovery, manipulation, energy-aware operation, fault diagnosis, and human–robot interaction. However, the literature on AI-driven quadruped robotics is distributed across diverse technical topics, robot platforms, validation settings, and performance metrics, making it difficult to assess the maturity and practical value of different approaches. To address this need, this review provides an AI-centered and deployment-oriented overview of quadruped robotics. A systematic literature search was conducted using Web of Science, IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink, covering studies published approximately from 2000 to 2025. After screening and eligibility assessment, 287 studies were included for detailed review. The review first examines AI-driven locomotion, including reinforcement learning, non-RL machine-learning methods, model-based approaches, and hybrid strategies, with attention to robustness, sim-to-real transfer, sensor use, computational requirements, and hardware validation. It then summarizes AI-supported advanced behaviors, including jumping, fall prevention and recovery, and object manipulation, focusing on reported quantitative performance, impact management, and reliability. Finally, it discusses system-level topics that affect real-world deployment, including fault diagnosis, energy-efficient control, shared autonomy, trust-aware and explainable interaction, and safety-aware human–robot collaboration. By organizing the literature according to robot capabilities, validation maturity, and deployment challenges, this review helps clarify the current progress, limitations, and future directions of AI-driven quadruped robots.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1901746</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1901746</link>
        <title><![CDATA[Subject-independent gait activity recognition using DSAF: dual-stream IMU-EMG attention fusion with asymmetric temporal encoding and physiological complementarity weighting]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhangyue Xu</author><author>Yan Zhu</author><author>Shurui Li</author>
        <description><![CDATA[IntroductionAccurate gait recognition using wearable sensors is of significant clinical value for adaptive prosthetic control, lower-limb exoskeleton assistance, and objective rehabilitation assessment. However, subject-independent recognition remains a major challenge, as unseen individuals can exhibit highly variable limb kinematics and muscle activation patterns, and existing approaches often rely on a single sensor modality or naive fusion strategies that fail to leverage the complementary information between inertial and electromyographic signals.MethodsTo address these gaps, this study proposes DSAF, a dual-stream attention fusion network that separately encodes kinematic (IMU) and neuromuscular (EMG) information, and adaptively integrates them through a physiological complementarity weighting mechanism designed for window-level modality adaptation. The framework is evaluated on the public HuGaDB dataset for eight common locomotion activities (e.g., walking, running, stair negotiation, and sit-to-stand transitions), using a leave-one-subject-out protocol to rigorously assess generalization to new users.ResultsDSAF achieves 96.41% accuracy, 96.08% macro-precision, 95.62% macro-recall, and 95.81% macro-F1, consistently outperforming recent sequence-learning baselines across all 18 held-out subjects. Ablation studies further confirm that both the modality-specific encoding and the adaptive fusion mechanism contribute positively to the performance.DiscussionThese findings indicate that adaptive IMU-EMG fusion can effectively strengthen wearable gait recognition, providing a promising solution for real-world rehabilitation monitoring and assistive human-machine interfaces.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1806512</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1806512</link>
        <title><![CDATA[Meta-analysis of the effects of lower extremity exoskeleton robot on the improvement of walking ability and curative effect in SCI patients]]></title>
        <pubdate>2026-07-21T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Peishi Feng</author><author>Jinglong Li</author><author>Sheng Lu</author><author>XuHua Xie</author><author>Feng Wang</author><author>Yuxiang Mao</author>
        <description><![CDATA[BackgroundSpinal cord injury (SCI) leads to significant motor and sensory impairments, reducing independence and quality of life. Conventional rehabilitation often lacks the intensity and task specificity required for optimal gait recovery. Powered lower-limb exoskeletons have emerged as a promising adjunct to enhance functional outcomes through repetitive, task-oriented training.ObjectiveThis meta-analysis aimed to evaluate the effectiveness and safety of exoskeleton-assisted rehabilitation compared with conventional therapy in improving walking ability, motor function, and activities of daily living in individuals with SCI.MethodsA systematic search of PubMed, Embase, Cochrane Library, Scopus, Web of Science, CNKI, Wanfang, and VIP databases was conducted for studies published between January 2016 and December 2024. Ten controlled trials involving 412 participants were included. Primary outcomes were Berg Balance Scale (BBS), 6-Minute Walk Distance (6MWD), Lower Extremity Motor Score (LEMS), Walking Index for Spinal Cord Injury II (WISCI-II) and Modified Barthel Index (MBI). Data were pooled using fixed- or random-effects models based on heterogeneity.ResultsExoskeleton-assisted rehabilitation significantly improved balance (BBS: MD 4.84, 95% CI 4.19–5.50; p < 0.001), walking endurance (6MWD: MD 31.09 m, 95% CI 27.25–34.93; p < 0.001), LEMS (MD 10.00, 95% CI 8.31–11.69; p < 0.001), gait independence (WISCI-II: MD 3.25, 95% CI 2.65–3.85; p < 0.001), and activities of daily living (MBI: MD 3.44, 95% CI 1.23–5.65; p = 0.003). Improvements were also observed in the ASIA lower-limb motor score (MD 7.28, 95% CI 6.44–8.12; p < 0.001). Substantial heterogeneity was present in some outcomes. No serious adverse events related to exoskeleton use were reported across the included studies.ConclusionExoskeleton-assisted rehabilitation is associated with statistically significant improvements in balance, walking performance, motor function, and functional independence in individuals with SCI compared with conventional therapy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1899676</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1899676</link>
        <title><![CDATA[BFMGHC: a boosted fuzzy manifold granule hypersurface classifier with local topology preservation]]></title>
        <pubdate>2026-07-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wei Li</author><author>Weiyong Si</author><author>Zhisong Liu</author><author>Yuping Song</author>
        <description><![CDATA[Drawing inspiration from the brain's neurocognitive mechanisms of information chunking and topographic mapping, adaptive decision-making requires neural-grounded architectures that are interpretable and resilient to uncertainty. In this paper, we propose a novel boosted fuzzy manifold granule hypersurface classifier (BFMGHC). The algorithm performs classification at the “information granule" level, realizing an intelligent modeling method that is closer to human cognition, more interpretable, and more accommodating of uncertainty. The classifier mainly consists of three main parts: (1) A manifold-based measurement method for samples that preserves local topological structure is designed, echoing the topographic representations in neural dynamics. Based on this, a global optimization clustering algorithm is proposed and integrated with the Dask framework to achieve scalable hierarchical parallel granulation from raw inputs to high-level semantic granules. (2) In the fuzzy manifold granule space, a measurement method and a hypersurface classifier are constructed, utilizing a particle swarm optimization method for parameter solving. (3) To improve interpretability, weights are assigned to different granules and base classifiers, resembling bio-inspired neuromodulation to ensure stable behavior. The proposed BFMGHC was verified on three financial risk assessment datasets in the UCI Machine Learning Repository (Default of Credit Card Clients, Bank Marketing, and German Credit Data) and achieved superior performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1884626</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1884626</link>
        <title><![CDATA[Multi-way radial consistency pre-training for event based optical flow]]></title>
        <pubdate>2026-07-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>An Feng</author><author>Tao Wenyin</author>
        <description><![CDATA[Optical flow estimation is a low-level module in computer vision, widely used in tasks such as visual odometry, autonomous driving, high dynamic range (HDR) imaging, and action recognition. Existing event-based optical flow estimation approaches suffer from scarcity of dense real-world datasets, while some unsupervised frameworks have reduced reliance on large-scale datasets by forward-backward consistency loss, they primarily exploit a 1D temporal reversal, while largely ignoring the rotational and scaling motions ubiquitous in robotics and automotive scenes. This work introduces radial consistency, a self-supervised pre-training framework that maps the event stream to log-polar coordinates and tessellates the spatial domain into K radial rings and L angular sectors, a shared encoder-decoder to predict four complementary flow fields whose cyclic sum is driven to zero, yielding a closed-loop constraint that generalizes the classical forward-backward check to 360° within a sector. Our core contribution, the radial consistency loss, is completely label-free, together with auxiliary terms, enabling self-supervised pre-training on large-scale event data. We optionally apply supervised fine-tuning on small labeled sets to adapt to specific domains, achieving competitive accuracy with fully supervised methods. Validation experiments on Multi Vehicle Stereo Event Camera (MVSEC) dataset demonstrate strong performance: our method achieves 0.67 EPE averaged across all sequences, surpassing E-RAFT (0.89 EPE) and EV-FlowNet (1.10 EPE), without any additional data. On the rotation-heavy indoor_flying3 sequence specifically, we achieve 0.93 EPE (fine-tuned) and 1.49 EPE (self-supervised only) vs. E-RAFT 1.66. We also improve upon E-RAFT in computational efficiency [55 frames per second (FPS) and 26 giga floating-point operations (GFLOPs) vs. 42 FPS and 38 GFLOPs], while requiring only minimal supervised fine-tuning.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1863193</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1863193</link>
        <title><![CDATA[PSRNet: a phase-guided frequency-domain structure reconstruction network for RGB-T salient object detection]]></title>
        <pubdate>2026-07-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Feng Xie</author><author>Junhong Zhou</author><author>Feng Gao</author><author>Xinqiang Ma</author><author>Chunyu Yang</author>
        <description><![CDATA[IntroductionRGB-T salient object detection remains challenging because visible and thermal features often show structural shifts, weak thermal boundaries, and background interference.MethodsThis study proposes PSRNet, a phase-guided frequency-domain structure reconstruction network. RGB and thermal features are decomposed into amplitude and latent-phase components in the Fourier domain. Reliable cross-modal structural cues are aligned through amplitude-weighted phase consistency, and boundary-related high-frequency responses are reconstructed with a bounded Gaussian high-pass gate before adaptive phase-modulated fusion and multi-scale supervision.ResultsOn VT1000, PSRNet achieved an S-measure of 0.903, an MAE of 0.028, and an F-measure of 0.794 at threshold 0.80, with a boundary IoU of 0.862.DiscussionThe results indicate improved structural preservation and boundary recovery under challenging RGB-T conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1839252</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1839252</link>
        <title><![CDATA[Distributed multi-robot LiDAR SLAM with ground-optimized preprocessing and slope-adaptive segmentation]]></title>
        <pubdate>2026-07-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yu Wang</author><author>Qiang Zou</author><author>Fei Wang</author>
        <description><![CDATA[Large-scale outdoor robot navigation increasingly demands SLAM systems capable of operating efficiently across diverse and challenging terrain. While single-robot approaches face inherent coverage and computational limitations, distributed multi-robot frameworks extend this capability through collaborative mapping—yet they still degrade in complex outdoor environments due to two unresolved challenges: redundant ground points in raw point clouds overload feature extraction and loop-closure matching, while fixed ground segmentation thresholds fail on sloped terrain causing misclassification and trajectory degradation. We address the first challenge by integrating ground segmentation preprocessing as a parallel stage for each robot within the distributed SLAM framework, reducing point cloud size by 50.78% and achieving a 21.4% RMSE improvement for Robot 0 (7.99 m → 6.28 m) compared to the unprocessed baseline. We address the second challenge with the proposed SAGS (Slope-Adaptive Ground Segmentation) module, which continuously monitors platform tilt via IMU orientation and dynamically interpolates ground segmentation parameters within a 5°–15° tilt range; SAGS recovers Robot 1 RMSE from 8.48 m to 6.23 m (26.5% improvement) on sloped terrain without flat-terrain penalty (GPS-validated 1.083 m RMSE on a public 612 m benchmark). Both contributions are validated through progressive three-stage ablation evaluation on a campus three-robot dataset (heterogeneous team: two wheeled ground robots and one legged quadruped, diverse terrain including sloped sections) and cross-validated on a public GPS benchmark (612 m, GPS ground truth), confirming the independent contribution of each system component.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1857152</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1857152</link>
        <title><![CDATA[A bionic intelligent method combining evolutionary game theory with particle swarm optimization for UAV 3D path planning]]></title>
        <pubdate>2026-06-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lixin Jia</author><author>Peng Shi</author>
        <description><![CDATA[Recently, UAV path planning in 3D complex environments has attracted increasing attention due to its significance in UAV motion control systems. However, the NP-hard nature of this problem poses significant challenges in generating a high-quality path. To address this issue, this paper proposes an improved self-adaptive particle swarm optimization (ISAPSO) algorithm by integrating the standard PSO 2011 with evolutionary game theory (EGT). Firstly, a novel self-adaptive parameter updating strategy is proposed, which combines the evolutionary stable strategy in EGT with hyperbolic tangent function to balance the exploration and exploitation capabilities of ISAPSO. Subsequently, an ISAPSO-based path planning approach is developed to generate optimal 3D path for UAV in an obstacle-rich environment. To efficiently handle constraints, a novel self-adaptive constraint handling technology is proposed in the developed path planner. Finally, the performance of the proposed ISAPSO is evaluated against six state-of-the-art evolutionary algorithms using 20 test functions. Following the benchmark study, the ISAPSO-based path planner is is validated in different scenarios against six well-known counterparts. The simulation results confirm that the proposed ISPASO outperforms its competitors in the benchmark study at a 90% confidence level. Moreover, the ISPASO-based path planning method dominates its contenders in terms of the path optimality. Therefore, the proposed method could be regarded as a vital alternative in the area of path planning.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1857548</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1857548</link>
        <title><![CDATA[Confidence-driven adaptive time window for real-time driver fatigue detection in Level 2-3 autonomous vehicles: a multi-dataset validation study]]></title>
        <pubdate>2026-06-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wantong Xie</author><author>Peng Xiao</author>
        <description><![CDATA[Driver fatigue constitutes a critical safety hazard in Level 2-3 (L2-3) conditionally automated vehicles, where the paradoxical demand for sustained supervisory vigilance despite minimal active engagement accelerates cognitive underload and impairs timely takeover readiness. Existing vision-based driver monitoring systems are constrained by fixed temporal analysis windows and binary classifiers that neither quantify prediction uncertainty nor adapt to the heterogeneity of real-world fatigue dynamics, resulting in elevated false alarm rates and poor cross-domain generalization. This study introduces a confidence-driven adaptive time window (CDATW) framework: a closed-loop neuro-computational pipeline that couples a lightweight MobileNetV3-CBAM-BiLSTM spatial–temporal encoder with a Monte Carlo Dropout uncertainty estimator to produce simultaneous fatigue probability and epistemic confidence outputs at each inference step. The confidence signal governs a window controller that contracts observation periods to 5–10 s under high certainty (confidence >0.85) for rapid warning, and extends them to 20–30 s under low certainty (confidence <0.60) to suppress spurious alarms—instantiating the feedback-driven adaptive sensing principle central to neurorobotic perception. The framework was validated on four heterogeneous public datasets (NTHU-DDD, YawDD, UTA-RLDD, and DROZY) under single-dataset, cross-dataset transfer, and mixed-dataset training protocols. Single-dataset accuracy ranged from 88.6 to 91.8% with AUC of 0.92–0.95, while the adaptive mechanism reduced false alarm rates by 35.2% relative to fixed 15-s baselines. The architecture sustains 38–45 FPS on an NVIDIA Jetson Xavier NX automotive embedded platform, and confidence calibration achieves an Expected Calibration Error of 0.078, with high-confidence predictions (>0.9) attaining 95.6% accuracy. These results demonstrate that uncertainty-aware adaptive temporal reasoning embedded in a deployable neurorobotic architecture constitutes a computationally efficient and practically viable strategy for driver state monitoring in L2-3 autonomous vehicles, with broader implications for closed-loop perception in safety-critical human-machine systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1849143</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1849143</link>
        <title><![CDATA[Learning robust and generalizable bimanual skills: a spatiotemporal causal hierarchical diffusion framework with attention anti-interference]]></title>
        <pubdate>2026-06-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xukun Liu</author><author>Fengjuan Xie</author><author>Zhenyu Liu</author><author>Guangning Li</author><author>Shenggang Wei</author><author>Kai Xu</author><author>Aifeng Liu</author>
        <description><![CDATA[IntroductionBimanual visuomotor imitation learning enables robots to acquire coordinated dual-arm manipulation skills from visual demonstrations, yet it faces significant challenges in temporal synchronization, spatial collision avoidance, long-horizon reasoning, and robustness to visual distractions. Existing diffusion-based policies often struggle to simultaneously capture long-horizon temporal dependencies and fine-grained spatial precision, while remaining sensitive to spurious correlations and domain shifts.MethodsTo address these limitations, we propose the Spatiotemporal Causal Hierarchical Diffusion Imitation Learner (SCH-DIL), a framework that integrates spatiotemporal hierarchical diffusion optimization to factorize the denoising process into temporal and spatial branches for multi-scale action modeling. The framework further incorporates causal visual representation learning that minimizes mutual information with confounding environmental factors to produce invariant features, along with noise-robust diffusion modeling that employs learnable observation uncertainty estimation and confidence-aware denoising. Additionally, attention anti-interference regularization is introduced to penalize distractions and enforce temporal attention consistency.ResultsExtensive experiments on the RoboTwin 2.0 benchmark demonstrate that SCH-DIL consistently outperforms existing diffusion-based and imitation learning baselines, achieving higher success rates under both clean and domain-randomized inference settings.DiscussionThese improvements are achieved with minimal computational overhead, suggesting that the proposed hierarchical and causally regularized diffusion framework offers a practical and robust solution for bimanual visuomotor imitation learning in visually challenging and domain-shifted environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1849093</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1849093</link>
        <title><![CDATA[DSPE-ViT: a lightweight vision transformer with dynamic sparse positional encoding for dense small object detection in UAV imagery]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liya Cai</author><author>Shuping Li</author>
        <description><![CDATA[BackgroundDetecting densely distributed small objects in unmanned aerial vehicle (UAV) aerial imagery poses a persistent challenge in computer vision. Vision Transformers (ViTs), empowered by global self-attention, perform strongly in object detection, but their fixed absolute positional encoding (PE) adapts poorly to scenes where small targets cluster at high density, and redundant encoding dimensions introduce unnecessary computational overhead.MethodsThis paper presents DSPE-ViT, a lightweight ViT-based detection framework tailored for dense small object detection in UAV imagery. Its core DSPE module comprises two complementary components: a PE Redundancy Pruner that employs learnable soft-gating masks to adaptively suppress redundant PE dimensions, and a Local PE Enhancer that introduces density-aware adaptive-window relative positional encoding to strengthen local spatial perception in high-density regions. Beyond the DSPE module, a Small Object Feature Pyramid Network (SmallObjFPN) integrating SE channel attention with depthwise separable convolutions improves multi-scale feature representation, and the WIoU v3 loss is adopted to refine bounding-box regression for small targets.ResultsOn the VisDrone2019-DET dataset, DSPE-ViT achieves 43.2% mAP@0.5 with only approximately 6.0 M parameters and 15.8 GFLOPs. Cross-domain evaluation on SeaDronesSee yields 30.1% mAP@0.5 under zero-shot transfer and 38.4% after fine-tuning.ConclusionThe cross-domain results confirm the generalization capability of the proposed lightweight framework.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1860170</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1860170</link>
        <title><![CDATA[ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation]]></title>
        <pubdate>2026-06-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xukun Liu</author><author>Fengjuan Xie</author><author>Kai Xu</author><author>Zhenyu Liu</author><author>Shenggang Wei</author><author>Guangning Li</author><author>Xu Sun</author>
        <description><![CDATA[IntroductionLearning robust and temporally consistent manipulation policies from long-horizon visual observations remains a fundamental challenge in imitation learning. While recent Transformer-based approaches reduce compounding errors via temporally extended action chunks, most methods rely on deterministic or unimodal action representations, limiting their ability to capture the inherent multimodality of expert demonstrations.MethodsIn this work, we propose ST-HONet, a Spatio-Temporal Hierarchical Network that integrates three adaptive optimization mechanisms operating jointly across spatial and temporal dimensions. ST-HONet formulates policy learning as conditional generation of extended action segments using a Transformer-based conditional variational autoencoder to explicitly model multimodal expert behaviors through a structured latent representation. To ensure stable optimization over long temporal horizons, we introduce a unified spatio-temporal training framework combining adaptive data augmentation, progressive latent regularization, and multi-stage optimization strategies.ResultsWe evaluate ST-HONet on RoboTwin 2.0, a large-scale benchmark for long-horizon bimanual manipulation under domain randomization with automatically generated expert demonstrations. Across representative manipulation tasks, ST-HONet achieves consistently higher task success rates compared to baseline models, while incurring only minimal additional computational overhead.DiscussionThese results demonstrate that explicitly modeling multimodality via a structured latent space, combined with joint spatio-temporal training mechanisms, significantly improves policy robustness and temporal consistency in long-horizon visuomotor imitation learning. The minimal computational overhead further supports the practical deployability of ST-HONet in real-world systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1850367</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1850367</link>
        <title><![CDATA[Exoskeleton robotics: from rigid structures to bio-integrated systems]]></title>
        <pubdate>2026-06-12T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Liancheng Zheng</author><author>Rizauddin Ramli</author><author>Wenfeng Zhang</author>
        <description><![CDATA[Exoskeleton robots have become a representative class of wearable robotic systems for rehabilitation, mobility assistance, occupational support, and human performance augmentation. As the field moves from laboratory prototypes toward clinical, industrial, and daily life deployment, research priorities are shifting from device-centered performance improvement to human-centered integration. This mini review provides a structured and critically oriented synthesis of exoskeleton technologies from four interconnected perspectives: technical architecture, technological paradigm evolution, deployment barriers, and future research directions. To improve transparency and reproducibility, we adopted a narrative review strategy with explicit literature selection criteria. Publications were identified from major scientific databases using combinations of keywords related to exoskeleton robotics, actuation, control, human-robot interaction, soft robotics, neural interfaces, rehabilitation, and wearable assistance. Representative studies were selected according to relevance, technical influence, clinical or engineering significance, and coverage of major technological paradigms. The review first analyzes three core technical dimensions-actuation systems, control strategies, and human-robot interaction which jointly determine the performance, adaptability, and usability of exoskeleton systems. Rather than only summarizing these technologies, we compare their trade-offs in terms of power density, control precision, compliance, energy efficiency, personalization, safety, and deployment readiness. The review then examines the evolution from rigid exoskeletons, which provide high structural support and precise force transmission, to soft exoskeletons, which improve compliance and comfort, and further to bio-integrated systems that combine neural interfaces, functional electrical stimulation, multimodal sensing, and mechanical assistance. Based on this synthesis, we organize the review using a Human–Exoskeleton Integration Maturity Framework spanning mechanical coupling, physical compliance, functional adaptation, and cognitive/bio-integrated coupling. Persistent barriers, including energy supply, personalization, safety assurance, cost, regulatory translation, and ethical governance, are critically discussed. Finally, future directions are outlined, including neural-interface-driven control, multimodal perception, human-in-the-loop optimization, hybrid rigid-soft architectures, and socially responsible design. Overall, this review argues that the next stage of exoskeleton development will depend not merely on stronger actuators or more intelligent algorithms, but on integrated systems that are adaptive, trustworthy, affordable, and seamlessly embedded in human movement and function.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1866481</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1866481</link>
        <title><![CDATA[EQISP: efficient quantized image signal processing with multi-scale pyramid fusion for resource constrained embodied perception]]></title>
        <pubdate>2026-06-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tongxin Yang</author><author>Ling Guo</author><author>Jie Li</author><author>Qi Qin</author><author>Bowen Liu</author><author>Qian Zhang</author>
        <description><![CDATA[IntroductionResource-constrained environmental perception requires autonomous robots and embodied intelligent systems to process visual signals efficiently while preserving image fidelity in complex real-world environments. However, converting high dynamic range RAW sensor data into perceptually faithful RGB images remains computationally expensive, thereby limiting the deployment of neural image signal processors on edge platforms with restricted memory, energy, and computational budgets.MethodsConsequently, this study proposes the enhanced quantized image signal processor (EQISP), comprising the quantized convolutional neural network (QCNN) and the unified pyramid fusion algorithm (UPFA). QCNN employs dynamic fixed-point hybrid quantization, which adjusts parameter ranges according to the linear relationship between threshold standard deviation and fractional length, thereby significantly reducing the computational load. Meanwhile, UPFA utilizes Gaussian pyramids to capture global illumination and Laplacian pyramids to preserve fine details, enabling multi-scale, multi-exposure fusion and iterative reconstruction to mitigate detail loss induced by quantization.ResultsComprehensive comparative experiments demonstrated that EQISP achieved a PSNR of 22.90 dB, an SSIM of 0.9278, and 164.843 GFLOPs. Compared with the PyNET baseline, EQISP improved the PSNR by 1.71 dB while reducing the computational cost by a factor of 4.24. Furthermore, deployment experiments on an NVIDIA Jetson TX2 development board showed that EQISP achieved a model size of 57 MB, an inference latency of 189 ms, an inference speed of 6.1 FPS, and a peak memory usage of 2.2 GB.DiscussionThese results provide practical evidence that EQISP can serve as an efficient and scalable visual front end for resource-constrained embodied perception systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnbot.2026.1846433</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnbot.2026.1846433</link>
        <title><![CDATA[ST-HADP: Spatio-Temporal hierarchical attention diffusion policy for long-horizon generalizable bimanual visuomotor imitation]]></title>
        <pubdate>2026-06-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xukun Liu</author><author>Fengjuan Xie</author><author>Shibo Liu</author><author>Xu Sun</author><author>Zhenyu Liu</author><author>Guangning Li</author><author>Shenggang Wei</author>
        <description><![CDATA[IntroductionDual-arm robotic manipulation presents fundamental challenges in coordinating spatially shared perception and temporally extended behaviors under limited demonstration settings. Existing diffusion-based visuomotor policies rely on flat temporal horizons and globally pooled visual features, which fail to capture the structured nature of bimanual collaboration.MethodsWe propose the Spatio-Temporal Hierarchical Attention Diffusion Policy (ST-HADP), a framework that extends 3D diffusion policies through explicit spatial and temporal structuring. ST-HADP introduces a Spatial Attention Module that learns arm-specific focus over task-relevant 3D regions, enabling dynamic and coordinated spatial reasoning. It further incorporates a Temporal Abstraction Module that models action sequences across multiple timescales via hierarchical latent variables, facilitating coarse-to-fine action generation aligned with the natural progression of long-horizon tasks. These components are jointly optimized with a multi-objective loss function that integrates attention regularization and temporal consistency, promoting spatially focused and temporally smooth coordination.ResultsWe evaluate ST-HADP on the RoboTwin 2.0 platform across six dual-arm robot configurations with diverse morphologies and tasks. Using only 50 automatically generated expert demonstrations, our method consistently outperforms baseline policies, achieving higher success rates with modest additional computational overhead.DiscussionThe results demonstrate that explicit spatial and temporal structuring enables effective dual-arm coordination under limited demonstration settings. ST-HADP provides a generalizable framework for bimanual manipulation, suggesting that hierarchical attention mechanisms offer a promising direction for sample-efficient learning of coordinated multi-arm behaviors.]]></description>
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