Biomimetic Intelligence and Robotics最新文献

{"title":"YOLO-FOA: A lightweight rotational target detection algorithm based on improved YOLO for optical fiber robot","authors":"Yingqi Wu,&nbsp;Jialong Chen,&nbsp;Xiuli Yu,&nbsp;Jian Li","doi":"10.1016/j.birob.2026.100273","DOIUrl":"10.1016/j.birob.2026.100273","url":null,"abstract":"<div><div>In fiber optic communication, as networks expand, precise detection and alignment of fiber optic adapters are crucial for enhancing system stability and transmission quality. Traditional target detection algorithms face two main issues in fiber optic adapter detection: inability to handle arbitrarily oriented targets and difficulty in efficient deployment on embedded devices. To tackle these issues, this paper introduces a lightweight rotating target detection algorithm, YOLO-FOA, for fiber optic communication scenarios. The algorithm is based on the YOLO model, which significantly reduces the computational and parametric quantities of the model by introducing Dynamic Head and Dynamic ATSS, and the C2f_MViTBv3, C2f_GhostBlockv2 modules and Angle DFL Loss are designed to improve the detection accuracy. In addition, the dynamic alignment correction mechanism can be effectively applied to intelligent calibration and real-time deviation correction in fiber optic communication networks. Experiments show YOLO-FOA achieves 97.1% detection accuracy on a self-constructed dataset, outperforming the baseline model by 1.3%, with a 4.5% reduction in parameters and 7.2% in computation. Suitable for embedded devices due to its high accuracy and low resource demands, YOLO-FOA offers a new approach to enhancing fiber optic communication system stability and transmission quality.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100273"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From knowing to doing: Learning diverse motor skills through instruction learning","authors":"Linqi Ye ,&nbsp;Yi Cheng ,&nbsp;Jiayi Li ,&nbsp;Xianhao Wang ,&nbsp;Bin Liang ,&nbsp;Yan Peng","doi":"10.1016/j.birob.2026.100286","DOIUrl":"10.1016/j.birob.2026.100286","url":null,"abstract":"<div><div>Recent years have witnessed many successful trials in the robot learning field. For contact-rich robotic tasks, it is challenging to learn coordinated motor skills by reinforcement learning. Imitation learning solves this problem by using a mimic reward to encourage the robot to track a given reference trajectory. However, imitation learning is not so efficient and may constrain the learned motion. In this paper, we propose instruction learning, which is inspired by the human learning process and is highly efficient, flexible, and versatile for robot motion learning. Instead of using a reference signal in the reward, instruction learning applies a reference signal directly as a feedforward action, and it is combined with a feedback action learned by reinforcement learning to control the robot. Besides, we propose the action bounding technique and remove the mimic reward, which is shown to be crucial for efficient and flexible learning. We compare the performance of instruction learning with imitation learning, indicating that instruction learning can greatly speed up the training process and guarantee learning the desired motion correctly. The effectiveness of instruction learning is validated through a bunch of motion learning examples for a biped robot and a quadruped robot, where skills can be learned typically within several million steps. Besides, we also conduct sim-to-real transfer and online learning experiments on a real quadruped robot. Instruction learning has shown great merits and potential, making it a promising alternative for imitation learning.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100286"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trajectory tracking and jumping control of quadruped via phase-aware iLQR controller","authors":"Shuomo Zhang ,&nbsp;Wei Zou ,&nbsp;Hu Su ,&nbsp;Chi Zhang ,&nbsp;Hongxuan Ma","doi":"10.1016/j.birob.2026.100284","DOIUrl":"10.1016/j.birob.2026.100284","url":null,"abstract":"<div><div>Jumping is a critical capability for quadruped robots, especially for navigating obstacles and gaps in complex environments. For successful jump, accurate trajectory tracking and robust feedback mechanism are essential, as cumulative deviations from the desired jumping trajectory can lead to instability or landing failure. Existing controllers often rely on fixed joint-level PD control or simplified inverse dynamics, which often fall short in tracking accuracy and robustness. In this paper, we propose a phase-aware iterative Linear Quadratic Regulator (iLQR) framework tailored for dynamic quadruped jumping tasks. By segmenting the jumping motion into distinct phases, we define phase-wise optimal control problem that respects the unique characteristics and requirements of each stage. Moreover, by leveraging a planar full-body dynamics of quadruped in each iLQR sub-problem, we derive a tracking controller consisting time-varying, full-state feedback gains, which shows better performance in tracking accuracy and disturbances rejection over traditional baseline controllers. Extensive simulation and hardware experiments on the Deeprobotics Lite3 quadruped validate the effectiveness and reliability of our proposed method in a number of dynamic jumping scenarios.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100284"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust visual semantic perception for flexible grinding of complex welds","authors":"Junjun Wu ,&nbsp;Weikun Qiu ,&nbsp;Jinjia Huang ,&nbsp;Haichu Chen","doi":"10.1016/j.birob.2026.100288","DOIUrl":"10.1016/j.birob.2026.100288","url":null,"abstract":"<div><div>Semantic segmentation methods based on RGB images exhibit notable limitations in complex industrial scenarios, particularly in addressing interference factors such as dynamic lighting variations and polymorphic weld seam morphologies, which lead to insufficient feature extraction capabilities and reduced segmentation accuracy and robustness. To address these limitations, this study proposes a polymorphic weld seam semantic segmentation model (PWSM) based on multi-level feature fusion, which effectively integrates the informational advantages of RGB and depth images to enhance perceptual capabilities in complex environments. The proposed model introduces a Dual-Stream Dual-modal Fusion (DSDF) module that employs channel selection and spatial selection strategies to extract and enhance complementary features from RGB and depth images. Concurrently, a Multi-Level Feature Fusion Module (ML-FFM) is developed to progressively integrate low-level and high-level semantic information through a multi-scale mechanism, refining boundary features while preserving the integrity of feature representation. Experimental results demonstrate that the model achieves superior segmentation performance on a complex multi-form weld seam dataset, particularly showing enhanced accuracy and robustness in challenging scenarios involving occlusions and illumination variations. Compared with existing single-modal and multi-modal models, the proposed model achieves performance improvements of 1.52% and 0.65%, respectively, providing effective technical support for intelligent perception of polymorphic weld seams.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100288"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved PRM algorithm based on dynamic partitioning and adaptive sampling","authors":"Yifan Wang,&nbsp;Xiangrong Zhao,&nbsp;Gang Chen,&nbsp;Xianyuan Gao,&nbsp;Kaichao Chen","doi":"10.1016/j.birob.2026.100283","DOIUrl":"10.1016/j.birob.2026.100283","url":null,"abstract":"<div><div>The Probabilistic Roadmap (PRM) algorithm has been widely employed in robotic manipulator path planning tasks due to its rapid exploration capabilities, particularly in high-dimensional configuration spaces with complex kinematic and environmental constraints. However, the efficiency of PRM is inherently constrained by the distribution of sampling points. In scenarios involving narrow passages, the sparsity of samples within such regions may significantly increase the likelihood of planning failure. In view of this, this paper proposes an improved PRM algorithm that is suitable for narrow channels with obstacles and can significantly improve the efficiency of path planning. First, a non-uniform partitioning strategy based on obstacle density is proposed to dynamically divide the sampling area to reduce the connection of redundant edges. Second, to address the sampling failure often encountered in narrow passages due to insufficient sample points, a weighted sampling adjustment strategy is proposed, which adaptively modifies the sampling density between narrow and open regions based on a comprehensive distance metric. Third, an adaptive variable step-size strategy is developed to dynamically adjust the connection steps between obstacle boundaries and open areas, further enhancing roadmap connectivity. By integrating the aforementioned strategies, the improved PRM algorithm proposed was applied in both two-dimensional and three-dimensional environments. The simulation results demonstrate that the method is capable of finding feasible paths in complex scenarios. Compared to the Lazy PRM and the OBPRM algorithms, the proposed approach achieves reductions of approximately 8.77% and 7.44% in path length and 9.00% and 5.74% in planning time, respectively. Finally, its effectiveness and superiority in robotic manipulator path planning were further validated through application to a 7-DOF manipulator.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100283"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A vision-based humanoid compliant skill transfer framework: Application to robotic cutting tasks","authors":"Zhaohong Mai ,&nbsp;Chao Zeng ,&nbsp;Ning Wang ,&nbsp;Chenguang Yang","doi":"10.1016/j.birob.2026.100280","DOIUrl":"10.1016/j.birob.2026.100280","url":null,"abstract":"<div><div>Autonomously completing a contact-rich task for multiple manipulation objects remains a challenging problem for robots. To achieve this goal, learning from demonstration has emerged as an efficient method for transferring human-like skills to robots. Existing works primarily focus on trajectory or impedance learning to design force-impedance controllers for specific tasks, which require precise force sensing. However, visual perception plays a critical role in enabling humans to perform dexterous manipulation. To bridge the gap between vision and learning in the control loop, this work proposes a vision-based humanoid compliant skill transfer (VHCST) framework. Considering the lack of vision-impedance mapping, a hybrid tree is introduced as a planning bridge to encode skill parameters across multiple objects. To simplify skill transfer, an observation-wearable demonstration method is employed to capture the position and stiffness of human’s arm. The decoupled learning model incorporates the geometric properties of stiffness ellipsoids, which reside on Riemannian manifolds. Finally, the proposed approach is validated through robotic cutting experiments involving multiple objects. Comparative experimental results demonstrate the effectiveness of the proposed framework.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100280"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language model-based task planning for service robots: A review","authors":"Shaohan Bian ,&nbsp;Ying Zhang ,&nbsp;Guohui Tian ,&nbsp;Zhiqiang Miao ,&nbsp;Edmond Q. Wu ,&nbsp;Simon X. Yang ,&nbsp;Changchun Hua","doi":"10.1016/j.birob.2026.100274","DOIUrl":"10.1016/j.birob.2026.100274","url":null,"abstract":"<div><div>With the rapid advancement of large language models (LLMs) and robotics, service robots are increasingly becoming an integral part of daily life, offering a wide range of services in complex environments. To deliver these services intelligently and efficiently, robust and accurate task planning capabilities are essential. This paper presents a comprehensive overview of the integration of LLMs into service robotics, with a particular focus on their role in enhancing robotic task planning. First, the development and foundational techniques of LLMs, including pre-training, fine-tuning, retrieval-augmented generation (RAG), and prompt engineering, are reviewed. We then explore the application of LLMs as the cognitive core—“brain”—of service robots, discussing how LLMs contribute to improved autonomy and decision-making. Furthermore, recent advancements in LLM-driven task planning across various input modalities are analyzed, including text, visual, audio, and multimodal inputs. Finally, we summarize key challenges and limitations in current research and propose future directions to advance the task planning capabilities of service robots in complex, unstructured domestic environments. This review aims to serve as a valuable reference for researchers and practitioners in the fields of artificial intelligence and robotics.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100274"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HDCAR: A 3D-2D registration network for abdominal aortic vessels based on CTA vessel models and DSA images","authors":"Bo Zhang ,&nbsp;Shiqi Liu ,&nbsp;Xiaoliang Xie ,&nbsp;Xiaohu Zhou ,&nbsp;Zengguang Hou ,&nbsp;Meng Song ,&nbsp;Xiyao Ma ,&nbsp;Kang Li ,&nbsp;Zhichao Lai ,&nbsp;Bao Liu","doi":"10.1016/j.birob.2025.100272","DOIUrl":"10.1016/j.birob.2025.100272","url":null,"abstract":"<div><div>Multimodal image registration is a crucial prerequisite for the automation and intelligence of interventional surgical medical robots. In endovascular aneurysm repair, due to limitations in imaging principles and hemodynamic effects, single-frame DSA images often fail to provide a complete representation of the vascular structure. This is particularly true for blood vessels that run parallel to the X-ray beam, as they are difficult to visualize in the DSA images. To address this issue, this study proposes an abdominal aortic vessel registration network, HDCAR, based on preoperative CTA 3D vascular models and intraoperative DSA images, aiming to enhance vascular completeness and spatial consistency in intraoperative imaging. The HDCAR network integrates multiple optimization modules to improve registration accuracy and robustness. First, the K-Sample module is employed to filter DSA images, enhancing the uniformity of intra-vascular structures and improving contrast between vessels and surrounding tissues. Second, depth information is incorporated to strengthen cross-dimensional spatial feature fusion, thereby optimizing the alignment between preoperative 3D models and intraoperative 2D images. Additionally, the network utilizes a dual-rectangular-window-based cross-attention mechanism and the RankC module to enhance both global contextual relationships and local feature representations. The ASPP module is further employed to extract multi-scale feature information, improving the model’s ability to capture vascular structures. Finally, a two-stage hybrid loss function is applied to optimize network parameters, ensuring precise and stable image registration. Experimental results demonstrate that the HDCAR network achieves high-precision vascular registration across multi-modal images, significantly improving the completeness and accuracy of intraoperative vascular imaging. This provides more precise imaging support for endovascular aneurysm repair procedures and holds great potential for clinical applications.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100272"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of efficient creeping locomotion for snake-like robots with compliant passive joints","authors":"Yiming Cao ,&nbsp;Longchuan Li ,&nbsp;Zhenxuan Ma ,&nbsp;Zaiyang Liu ,&nbsp;Atsushi Kakogawa ,&nbsp;Shugen Ma ,&nbsp;Zhongkui Wang","doi":"10.1016/j.birob.2026.100281","DOIUrl":"10.1016/j.birob.2026.100281","url":null,"abstract":"<div><div>Snake-like robots leverage their slender bodies to navigate confined spaces by coordinating the multiple actuated joints, which enable effective movement through constrained pathways. However, their high degrees of freedom in fully actuated systems engender significant challenges in reducing energy consumption. To address these challenges, this paper derives insights from the muscle functions of biological snakes and investigates the integration of compliance passive joints into snake-like robots, with the aim of enhancing locomotion efficiency. Passive joints, equipped with torsional springs, facilitate indirect actuation through energy storage and release. Under such background, we propose a dynamic model to investigate the influence of passive joints on locomotion performance. Simulations are utilized to analyze the effects of varying spring stiffness beyond experimental constraints. To facilitate systematic validation, a modular snake-like robot is designed. It allows flexible joint configurations, reassembly, and adjustable joint placements. Additionally, passive joint mechanism is refined to eliminate the requirements for motor gear reconfiguration, thereby improving experimental adaptability. The proposed model is evaluated through simulations and experiments to investigate the effects of joint stiffness on locomotion speed, while energy efficiency is analyzed experimentally. The results reveal that appropriate stiffness parameters significantly enhance motion efficiency. Moreover, the placement of passive joints plays a key role in the robot’s motion performance. Among all configurations, a compliant passive tail joint with an appropriate spring setup achieves the best performance. It increases motion speed by 26.8% and reduces energy consumption by 52.2%. These findings provide insights into the role of passive joints in snake-like robots, potentially contributing to future design improvements in locomotion efficiency and adaptability.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100281"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-to-end replay-based trajectory planning for autonomous vehicles under multi-weather scenarios","authors":"Jinjun Dun ,&nbsp;Yuenan Zhao ,&nbsp;Xiaoyu Xu ,&nbsp;Zhenguo Chen ,&nbsp;Hui Xie","doi":"10.1016/j.birob.2026.100275","DOIUrl":"10.1016/j.birob.2026.100275","url":null,"abstract":"<div><div>Autonomous driving systems face challenges from perception degradation and kinematic coupling in adverse weather. This paper introduces an end-to-end trajectory prediction framework integrating multi-weather continual learning with kinematic constraint optimization. Traditional weather-specific models suffer from fragmented experience and catastrophic forgetting, impacting control in low-visibility, high-curvature scenarios. We propose a multi-weather adaptive replay mechanism (MWARM) with entropy-weighted sampling for cross-weather knowledge transfer, paired with a bird’s eye view (BEV)-based perception-planning architecture using multi-objective model predictive control (MO-MPC) to adjust weights based on real-time curvature and weather data. Evaluated in CARLA with a multi-weather dataset, the framework provides a robust solution for complex conditions.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100275"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}