Frontiers in Neurorobotics最新文献

{"title":"AI-driven quadruped robots: from fundamental locomotion to advanced biomimetic behaviors.","authors":"Likai Wu, Chee-Onn Chow, Wei Ru Wong, Joon Huang Chuah, Jeevan Kanesan, Meina Zhang","doi":"10.3389/fnbot.2026.1855550","DOIUrl":"10.3389/fnbot.2026.1855550","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1855550"},"PeriodicalIF":3.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13442555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148684325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morphology-agnostic humanoid retargeting via perception-motivated graph similarity.","authors":"Chaojie Fu, Chengkai Su, Lei Jiang, Kaixin Lan, Yongbin Jin, Hongtao Wang","doi":"10.3389/fnbot.2026.1814977","DOIUrl":"10.3389/fnbot.2026.1814977","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1814977"},"PeriodicalIF":3.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13442438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148684247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subject-independent gait activity recognition using DSAF: dual-stream IMU-EMG attention fusion with asymmetric temporal encoding and physiological complementarity weighting.","authors":"Zhangyue Xu, Yan Zhu, Shurui Li","doi":"10.3389/fnbot.2026.1901746","DOIUrl":"10.3389/fnbot.2026.1901746","url":null,"abstract":"<p><strong>Introduction: </strong>Accurate 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.</p><p><strong>Methods: </strong>To 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.</p><p><strong>Results: </strong>DSAF 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.</p><p><strong>Discussion: </strong>These 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.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1901746"},"PeriodicalIF":3.5,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13437548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148678139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meta-analysis of the effects of lower extremity exoskeleton robot on the improvement of walking ability and curative effect in SCI patients.","authors":"Peishi Feng, Jinglong Li, Sheng Lu, XuHua Xie, Feng Wang, Yuxiang Mao","doi":"10.3389/fnbot.2026.1806512","DOIUrl":"10.3389/fnbot.2026.1806512","url":null,"abstract":"<p><strong>Background: </strong>Spinal 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.</p><p><strong>Objective: </strong>This 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.</p><p><strong>Methods: </strong>A 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.</p><p><strong>Results: </strong>Exoskeleton-assisted rehabilitation significantly improved balance (BBS: MD 4.84, 95% CI 4.19-5.50; <i>p</i> < 0.001), walking endurance (6MWD: MD 31.09 m, 95% CI 27.25-34.93; <i>p</i> < 0.001), LEMS (MD 10.00, 95% CI 8.31-11.69; <i>p</i> < 0.001), gait independence (WISCI-II: MD 3.25, 95% CI 2.65-3.85; <i>p</i> < 0.001), and activities of daily living (MBI: MD 3.44, 95% CI 1.23-5.65; <i>p</i> = 0.003). Improvements were also observed in the ASIA lower-limb motor score (MD 7.28, 95% CI 6.44-8.12; <i>p</i> < 0.001). Substantial heterogeneity was present in some outcomes. No serious adverse events related to exoskeleton use were reported across the included studies.</p><p><strong>Conclusion: </strong>Exoskeleton-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.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1806512"},"PeriodicalIF":3.5,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148673321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BFMGHC: a boosted fuzzy manifold granule hypersurface classifier with local topology preservation.","authors":"Wei Li, Weiyong Si, Zhisong Liu, Yuping Song","doi":"10.3389/fnbot.2026.1899676","DOIUrl":"10.3389/fnbot.2026.1899676","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1899676"},"PeriodicalIF":3.5,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148653578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-way radial consistency pre-training for event based optical flow.","authors":"An Feng, Tao Wenyin","doi":"10.3389/fnbot.2026.1884626","DOIUrl":"https://doi.org/10.3389/fnbot.2026.1884626","url":null,"abstract":"<p><p>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 <i>K</i> radial rings and <i>L</i> 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 <i>core contribution</i>, 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.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1884626"},"PeriodicalIF":3.5,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13422378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PSRNet: a phase-guided frequency-domain structure reconstruction network for RGB-T salient object detection.","authors":"Feng Xie, Junhong Zhou, Feng Gao, Xinqiang Ma, Chunyu Yang","doi":"10.3389/fnbot.2026.1863193","DOIUrl":"10.3389/fnbot.2026.1863193","url":null,"abstract":"<p><strong>Introduction: </strong>RGB-T salient object detection remains challenging because visible and thermal features often show structural shifts, weak thermal boundaries, and background interference.</p><p><strong>Methods: </strong>This 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.</p><p><strong>Results: </strong>On 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.</p><p><strong>Discussion: </strong>The results indicate improved structural preservation and boundary recovery under challenging RGB-T conditions.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1863193"},"PeriodicalIF":3.5,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13381454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148601840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed multi-robot LiDAR SLAM with ground-optimized preprocessing and slope-adaptive segmentation.","authors":"Yu Wang, Qiang Zou, Fei Wang","doi":"10.3389/fnbot.2026.1839252","DOIUrl":"https://doi.org/10.3389/fnbot.2026.1839252","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1839252"},"PeriodicalIF":3.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13368906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148455521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bionic intelligent method combining evolutionary game theory with particle swarm optimization for UAV 3D path planning.","authors":"Lixin Jia, Peng Shi","doi":"10.3389/fnbot.2026.1857152","DOIUrl":"10.3389/fnbot.2026.1857152","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1857152"},"PeriodicalIF":3.5,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13357615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148436369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confidence-driven adaptive time window for real-time driver fatigue detection in Level 2-3 autonomous vehicles: a multi-dataset validation study.","authors":"Wantong Xie, Peng Xiao","doi":"10.3389/fnbot.2026.1857548","DOIUrl":"10.3389/fnbot.2026.1857548","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1857548"},"PeriodicalIF":3.5,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13337876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148404400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}