Biomimetic Intelligence and Robotics最新文献_第2页

End-to-end replay-based trajectory planning for autonomous vehicles under multi-weather scenarios 基于端到端回放的多天气自动驾驶车辆轨迹规划

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.birob.2026.100275

Jinjun Dun , Yuenan Zhao , Xiaoyu Xu , Zhenguo Chen , Hui Xie

引用次数: 0

Design and testing of an orthopedic robot for deformity correction 一种矫形机器人的设计与测试

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.birob.2026.100279

Xiaoyong Wu , Cao Yang , Jie Wang , Jie Zhou , Kanglai Tang , Lin Guo

{"title":"Design and testing of an orthopedic robot for deformity correction","authors":"Xiaoyong Wu , Cao Yang , Jie Wang , Jie Zhou , Kanglai Tang , Lin Guo","doi":"10.1016/j.birob.2026.100279","DOIUrl":"10.1016/j.birob.2026.100279","url":null,"abstract":"<div><div>Deformity correction has positive significance for limb function reconstruction. To reduce the workload of the physicians and enhance the intelligence level of deformity correction, a wearable orthopedic robot based on traditional deformity correction devices is proposed, and the methodology for deformity correction is studied. By utilizing the homogeneous coordinate transformation method, the inverse kinematics of the robot is derived. Based on the approach of PointNet++, the small-batch gradient descent optimization method is used to train the model, achieving effective segmentation of the point cloud of the robotic system. The mirrored registration strategy based on the healthy contralateral bone is adopted, with the SAC-IA (Sample Consensus Initial Alignment) method for coarse registration and the ICP (Iterative Closest Point) method for fine registration, to measure the deformity parameters of the bone accurately. A physical prototype of the orthopedic robot is constructed, relevant experimental parameters are obtained through the optical measurement equipment, and the robot is driven to perform the deformity correction task based on the inverse kinematics solution. Experimental results confirm the clinical viability of the proposed orthopedic robot.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100279"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173688","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}

引用次数: 0

A bio-inspired intestine robot utilizing soft origami Kresling structures to imitate peristaltic wave motion and transport 仿生肠道机器人，利用软折纸Kresling结构来模拟蠕动波运动和传输

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.birob.2026.100282

Manjia Su , Beibin Liang , Haoxu Chen , Jing Lan , Jinhui Zhou , Hua Zhong , Yijiong Lin , Chunlong Wang , Ruiwei Liu

{"title":"A bio-inspired intestine robot utilizing soft origami Kresling structures to imitate peristaltic wave motion and transport","authors":"Manjia Su , Beibin Liang , Haoxu Chen , Jing Lan , Jinhui Zhou , Hua Zhong , Yijiong Lin , Chunlong Wang , Ruiwei Liu","doi":"10.1016/j.birob.2026.100282","DOIUrl":"10.1016/j.birob.2026.100282","url":null,"abstract":"<div><div>This paper presents a single-drive bio-inspired intestine robot that leverages the coupled rotational-contraction dynamics of Kresling origami structures and unidirectional valves to replicate intestinal peristalsis for directional transport. The minimalist design, comprising a servo motor, antagonistic chiral Kresling units, and check valves, enables continuous peristaltic wave propagation through reciprocal torsional input. Experimental validation demonstrates exceptional performance: transport speeds up to 20.91 mm/s, load capacity exceeding 97 g, and adaptability to objects spanning 32–46 mm in diameter across inclinations of 0°–90°. Key innovations include: (1) Biomechanical mimicry through antagonistic chiral units that convert rotation into radial contractions, replicating segmented intestinal propulsion; (2) Performance breakthroughs in speed and payload, enabled by efficient energy transfer from torsional kinematics; and (3) Valve-enabled directionality ensuring net forward displacement. Theoretical analysis establishes geometric constraints for valve-mediated transport, explaining the optimized operating range via valve aperture dynamics and material compliance. This work advances gastrointestinal robotics by addressing critical limitations in existing simulators: complex actuation, slow transport, and directional instability, providing a robust platform for medical applications such as segment artificial intestines replacement.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100282"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173684","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}

引用次数: 0

Enhanced reasoning and task planning for surgical autonomy using multi-modal large language models with gradual learning 使用渐进学习的多模态大语言模型增强手术自主性的推理和任务规划

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.birob.2026.100277

Sadra Zargarzadeh , Jemima Okanlawon , Maryam Mirzaei , Mahan Mohammadi , Mahdi Tavakoli

{"title":"Enhanced reasoning and task planning for surgical autonomy using multi-modal large language models with gradual learning","authors":"Sadra Zargarzadeh , Jemima Okanlawon , Maryam Mirzaei , Mahan Mohammadi , Mahdi Tavakoli","doi":"10.1016/j.birob.2026.100277","DOIUrl":"10.1016/j.birob.2026.100277","url":null,"abstract":"<div><div>Large language models (LLMs) have been widely adopted in robotic applications in recent years, but their ability in task planning of long-horizon and complex tasks remains a challenge. In this work, we present a gradual learning method to address this challenge and explore its usability in surgical training tasks that require high levels of reasoning, such as peg transfer and the sliding puzzle task. Experiments were conducted using the da Vinci Research Kit (dVRK), with environment feedback initiating follow-up prompts for the LLM when necessary, as well as in a simulation environment. Results showed that for complex tasks, the gradual learning method outperformed the direct approach in the LLM’s task and motion planning, requiring fewer follow-up prompts and leading to higher success rates with faster execution. This suggests that for complex pseudo-surgical tasks, it is more efficient to have the LLM solve simpler versions of the task while incrementally increasing complexity, rather than tackling the full complex task at once. The approach shows promise for enhancing robot-assisted surgery where tasks are complex, long-horizon, and demand high-reasoning abilities.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100277"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079434","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}

引用次数: 0

Optimization-based automated generation of 1-DOF multi-section continuum robots with predefined end-effector poses 基于优化的末端执行器位姿1自由度多截面连续体机器人自动生成

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.birob.2026.100276

Jiake Fu, Zengwei Wang, Felix Pancheri, Tim C. Lueth, Yilun Sun

{"title":"Optimization-based automated generation of 1-DOF multi-section continuum robots with predefined end-effector poses","authors":"Jiake Fu, Zengwei Wang, Felix Pancheri, Tim C. Lueth, Yilun Sun","doi":"10.1016/j.birob.2026.100276","DOIUrl":"10.1016/j.birob.2026.100276","url":null,"abstract":"<div><div>Continuum robots have been widely utilized in various fields, such as medical surgery, industrial manufacturing, and aerospace, due to their flexibility and compliance. However, their high structural compliance also presents significant challenges in achieving precise control. Although many existing continuum robots feature multiple degrees-of-freedom (DOFs) and complex control systems, such sophistication is often unnecessary for simple, repetitive, and task-specific applications where task-specific structures are more efficient. To address this issue, this paper proposes a parametric optimization-based automated design framework to generate structural models for multi-section 1-DOF flexure-joint-based continuum robots capable of achieving any two predefined end-effector poses. The proposed methodology employs a constant curvature assumption to simulate the bending characteristics of the continuum robot. MATLAB is used to optimize and solve the structural parameters, followed by the generation of 3D-printable models using the Solid Geometry Library Toolbox. Experimental results demonstrate that, under certain geometric boundary conditions for structural parameters, the robot’s end-effector can reach any two predefined poses with high accuracy. This approach significantly reduces the structural and control complexity of task-specific continuum robots, lowers manufacturing costs, and expands their range of applications.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100276"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079428","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}

引用次数: 0

A feedforward tendon-elongation compensator for tendon-sheath mechanisms with arbitrary and time-varying transmission routes in three-dimensional space 一种适用于三维空间中具有任意时变传输路径的肌腱鞘机构的前馈肌腱伸长补偿器

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.birob.2026.100278

Qian Gao , Jiaqi Li

{"title":"A feedforward tendon-elongation compensator for tendon-sheath mechanisms with arbitrary and time-varying transmission routes in three-dimensional space","authors":"Qian Gao , Jiaqi Li","doi":"10.1016/j.birob.2026.100278","DOIUrl":"10.1016/j.birob.2026.100278","url":null,"abstract":"<div><div>Tendon-sheath mechanisms (TSMs) are widely used for position transmission in robotic systems that require compactness and adaptability to complex environments. However, friction-induced tendon-elongation disrupts the alignment between input and output positions, preventing the robotic end-effector from accurately following motion commands. Since tendon-elongation depends on the configuration of the transmission route, resolving position transmission misalignment in TSMs becomes even more challenging. Building upon the tendon-elongation compensator developed in the author’s recent work, this study presents a technical note aiming to align the actual output position with the desired position. The improved compensator operates without relying on any distal sensory feedback, thereby preserving the compactness of the system. Notably, it is applicable to TSMs with arbitrary and time-varying transmission routes in three-dimensional (3-D) space, fulfilling the adaptability requirement. Preliminary experimental results demonstrate the potential of the presented technique, achieving 96.44%–97.56% accuracy in distal position tracking. By tackling a long-standing challenge in TSM research, this study lays a technical foundation for future advancements in the field.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"6 1","pages":"Article 100278"},"PeriodicalIF":5.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981363","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}

引用次数: 0

Construction and control of bio-syncretic robots actuated by living materials 生物材料驱动的生物合成机器人的构造与控制

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2025-12-01 Epub Date: 2025-09-02 DOI: 10.1016/j.birob.2025.100263

Yuyang Li , Chuang Zhang , Qi Zhang , Jiaduo Guo , Lianchao Yang , Wenfeng Liang , Lianqing Liu

引用次数: 0

Efficient co-adaptation of humanoid robot design and locomotion control using surrogate-guided optimization 基于代理导向优化的仿人机器人设计与运动控制的高效协同适应

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2025-12-01 Epub Date: 2025-08-13 DOI: 10.1016/j.birob.2025.100255

Yidong Du, Xuechao Chen, Zhangguo Yu, Fei Meng, Zishun Zhou, Yuanxi Zhang, Qingqing Li, Qiang Huang

{"title":"Efficient co-adaptation of humanoid robot design and locomotion control using surrogate-guided optimization","authors":"Yidong Du, Xuechao Chen, Zhangguo Yu, Fei Meng, Zishun Zhou, Yuanxi Zhang, Qingqing Li, Qiang Huang","doi":"10.1016/j.birob.2025.100255","DOIUrl":"10.1016/j.birob.2025.100255","url":null,"abstract":"<div><div>Recent advancements in reinforcement learning (RL) and computational resources have demonstrated the efficacy of data-driven methodologies for robotic locomotion control and physical design optimization, providing a scalable alternative to traditional human-crafted design paradigms. However, existing co-design approaches face a critical challenge: the computational intractability of exploring high-dimensional design spaces, exacerbated by the resource-intensive nature of policy training and candidate design evaluations. To address this limitation, we propose an efficient co-adaptation framework for humanoid robot kinematics optimization. Building on a bi-level optimization architecture that jointly optimizes mechanical designs and control policies, our method achieves computational efficiency through two synergistic strategies: (1) a universal policy generalizable across design variations, and (2) a surrogate-assisted fitness evaluation mechanism. We implement the method with humanoid robot Kuafu, and by experimental results we demonstrate the proposed method effectively reduces the cost and the optimized design can achieve near-optimal performance.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 4","pages":"Article 100255"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416756","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}

引用次数: 0

Humanoid dexterous hands from structure to gesture semantics for enhanced human–robot interaction: A review 从结构到手势语义增强人机交互的类人灵巧手：综述

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2025-12-01 Epub Date: 2025-08-20 DOI: 10.1016/j.birob.2025.100258

Xin Li , Wenfu Xu , Zaiqiao Ye , Han Yuan

{"title":"Humanoid dexterous hands from structure to gesture semantics for enhanced human–robot interaction: A review","authors":"Xin Li , Wenfu Xu , Zaiqiao Ye , Han Yuan","doi":"10.1016/j.birob.2025.100258","DOIUrl":"10.1016/j.birob.2025.100258","url":null,"abstract":"<div><div>As human–robot interaction (HRI) technology advances, dexterous robotic hands are playing a dual role—serving both as tools for manipulation and as channels for non-verbal communication. While much of the existing research emphasizes improving grasping and structural dexterity, the semantic dimension of gestures and its impact on user experience has been relatively overlooked. Studies from HRI and cognitive psychology consistently show that the naturalness and cognitive empathy of gestures significantly influence user trust, satisfaction, and engagement. This shift reflects a broader transition from mechanically driven designs toward cognitively empathic interactions — robots’ ability to infer human affect, intent, and social context to generate appropriate nonverbal responses. In this paper, we argue that large language models (LLMs) enable a paradigm shift in gesture control — from rule-based execution to semantic-driven, context-aware generation. By leveraging LLMs and visual-language models, robots can interpret environmental and social cues, dynamically map emotions, and generate gestures aligned with human communication norms. We conducted a comprehensive review of research in dexterous hand mechanics, gesture semantics, and user experience evaluation, integrating insights from linguistics and cognitive science. Furthermore, we propose a closed-loop framework — “perception–cognition–generation–assessment” — to guide gesture design through iterative, multimodal feedback. This framework lays the conceptual foundation for building universal, adaptive, and emotionally intelligent gesture systems in future human–robot interaction.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 4","pages":"Article 100258"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416760","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}

引用次数: 0

Joint moment estimation for hip exoskeleton control: A generalized moment feature generation method 髋部外骨骼控制关节力矩估计：一种广义矩特征生成方法

IF 5.4

Biomimetic Intelligence and Robotics Pub Date : 2025-12-01 Epub Date: 2025-06-09 DOI: 10.1016/j.birob.2025.100246

Yuanwen Zhang , Jingfeng Xiong , Haolan Xian , Chuheng Chen , Xinxing Chen , Haipeng Liang , Chenglong Fu , Yuquan Leng

{"title":"Joint moment estimation for hip exoskeleton control: A generalized moment feature generation method","authors":"Yuanwen Zhang , Jingfeng Xiong , Haolan Xian , Chuheng Chen , Xinxing Chen , Haipeng Liang , Chenglong Fu , Yuquan Leng","doi":"10.1016/j.birob.2025.100246","DOIUrl":"10.1016/j.birob.2025.100246","url":null,"abstract":"<div><div>Hip joint moments during walking are the key foundation for hip exoskeleton assistance control. Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation, such as simple sensor requirements and adaptability to variable walking speeds. However, existing joint moment estimation methods still suffer from a lack of personalization, leading to estimation accuracy degradation for new users. To address the challenges, this paper proposes a hip joint moment estimation method based on generalized moment features (GMF). A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder. Utilizing this well-featured representation, a GRU-based neural network is used to predict GMF with joint kinematics data, which can easily be acquired by hip exoskeleton encoders. The proposed estimation method achieves a root mean square error of 0.1180 ± 0.0021 Nm/kg under 28 walking speed conditions on a treadmill dataset, improved by 6.5% compared to the model without body parameter fusion, and by 8.3% for the conventional fusion model with body parameter. Furthermore, the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5% metabolic reduction (<span><math><mrow><mi>p</mi><mo><</mo><mn>0</mn><mo>.</mo><mn>01</mn></mrow></math></span>) for users compared to assist-off condition in level-ground walking.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 4","pages":"Article 100246"},"PeriodicalIF":5.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320350","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}

引用次数: 0