Pub Date : 2026-01-14DOI: 10.1016/j.birob.2026.100276
Jiake Fu, Zengwei Wang, Felix Pancheri, Tim C. Lueth, Yilun Sun
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.
{"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":"2026-01-14","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}
Pub Date : 2026-01-13DOI: 10.1016/j.birob.2026.100278
Qian Gao , Jiaqi Li
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.
{"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":"2026-01-13","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}
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.
{"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":"2026-01-13","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}
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.
{"title":"Large language model-based task planning for service robots: A review","authors":"Shaohan Bian , Ying Zhang , Guohui Tian , Zhiqiang Miao , Edmond Q. Wu , Simon X. Yang , 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":"2026-01-10","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}
Pub Date : 2025-12-31DOI: 10.1016/j.birob.2025.100272
Bo Zhang , Shiqi Liu , Xiaoliang Xie , Xiaohu Zhou , Zengguang Hou , Meng Song , Xiyao Ma , Kang Li , Zhichao Lai , Bao Liu
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.
{"title":"HDCAR: A 3D-2D registration network for abdominal aortic vessels based on CTA vessel models and DSA images","authors":"Bo Zhang , Shiqi Liu , Xiaoliang Xie , Xiaohu Zhou , Zengguang Hou , Meng Song , Xiyao Ma , Kang Li , Zhichao Lai , 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":"2025-12-31","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}
Pub Date : 2025-09-10DOI: 10.1016/j.birob.2025.100264
Aohua Li, Ye Zhou, Weijie Kuang, Hann Woei Ho
Micro Air Vehicle (MAV) swarms are often constrained by limited onboard processing capabilities and payload capacity, restricting the use of sophisticated localization systems. Lightweight ultra-wideband (UWB) ranging techniques are commonly used to estimate inter-vehicle distances, but they do not provide local bearing information—essential for precise relative positioning. Inspired by bat echolocation in low-visibility environments, we propose an acoustic-enhanced method for local bearing estimation designed for low-cost MAVs. Our approach leverages ambient acoustic signals naturally emitted by a target MAV in flight, combined with UWB distance measurements. The acoustic data is processed using the Frequency-Sliding Generalized Cross-Correlation (FS-GCC) method, enhanced with our analytical formulation that compensates for inter-channel switching delays in asynchronous, high-frequency sampling. This enables accurate Time Difference of Arrival (TDOA) estimation, even with compact microphone arrays. These TDOA values, along with known microphone geometry and UWB data, are integrated into our geometric model to estimate the bearing of the target MAV. We validate our approach in a controlled indoor hall across two experimental scenarios: static-bearing estimation, where the target MAV hovers at predefined angular positions (0°, ±30°, ±45°, ±60°), and dynamic-bearing estimation, where it flies across angles at varying velocities. The results show that our method yields reliable TDOA measurements compared to classical and machine learning baselines, and produces accurate bearing estimates in both static and dynamic settings. This demonstrates the feasibility of our low-cost acoustic-enhanced solution for local bearing estimation in MAV swarms, supporting improved relative navigation and decentralized perception in GPS-denied or visually degraded environments.
{"title":"Acoustic-enhanced local bearing estimation using low-cost microphones for Micro Air Vehicle swarms","authors":"Aohua Li, Ye Zhou, Weijie Kuang, Hann Woei Ho","doi":"10.1016/j.birob.2025.100264","DOIUrl":"10.1016/j.birob.2025.100264","url":null,"abstract":"<div><div>Micro Air Vehicle (MAV) swarms are often constrained by limited onboard processing capabilities and payload capacity, restricting the use of sophisticated localization systems. Lightweight ultra-wideband (UWB) ranging techniques are commonly used to estimate inter-vehicle distances, but they do not provide local bearing information—essential for precise relative positioning. Inspired by bat echolocation in low-visibility environments, we propose an acoustic-enhanced method for local bearing estimation designed for low-cost MAVs. Our approach leverages ambient acoustic signals naturally emitted by a target MAV in flight, combined with UWB distance measurements. The acoustic data is processed using the Frequency-Sliding Generalized Cross-Correlation (FS-GCC) method, enhanced with our analytical formulation that compensates for inter-channel switching delays in asynchronous, high-frequency sampling. This enables accurate Time Difference of Arrival (TDOA) estimation, even with compact microphone arrays. These TDOA values, along with known microphone geometry and UWB data, are integrated into our geometric model to estimate the bearing of the target MAV. We validate our approach in a controlled indoor hall across two experimental scenarios: static-bearing estimation, where the target MAV hovers at predefined angular positions (0°, ±30°, ±45°, ±60°), and dynamic-bearing estimation, where it flies across angles at varying velocities. The results show that our method yields reliable TDOA measurements compared to classical and machine learning baselines, and produces accurate bearing estimates in both static and dynamic settings. This demonstrates the feasibility of our low-cost acoustic-enhanced solution for local bearing estimation in MAV swarms, supporting improved relative navigation and decentralized perception in GPS-denied or visually degraded environments.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 4","pages":"Article 100264"},"PeriodicalIF":5.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416754","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}
Pub Date : 2025-09-02DOI: 10.1016/j.birob.2025.100263
Yuyang Li , Chuang Zhang , Qi Zhang , Jiaduo Guo , Lianchao Yang , Wenfeng Liang , Lianqing Liu
Bio-syncretic robots represent a novel class of robots that integrate biological and artificial materials. These robots combine the high energy efficiency and environmental adaptability of biological tissues with the precise control and programmability of traditional robots, making them a focal point in the field of robotics. This paper reviews the latest research progress in bio-syncretic robots. Initially, we classify and introduce bio-syncretic robots from the perspective of structural design, which incorporates both biological and artificial materials. Subsequently, we provide a detailed discussion of their fabrication techniques and control methodologies. Finally, to facilitate broader applications of bio-syncretic robots, this paper explores their potential applications and future development prospects.
{"title":"Construction and control of bio-syncretic robots actuated by living materials","authors":"Yuyang Li , Chuang Zhang , Qi Zhang , Jiaduo Guo , Lianchao Yang , Wenfeng Liang , Lianqing Liu","doi":"10.1016/j.birob.2025.100263","DOIUrl":"10.1016/j.birob.2025.100263","url":null,"abstract":"<div><div>Bio-syncretic robots represent a novel class of robots that integrate biological and artificial materials. These robots combine the high energy efficiency and environmental adaptability of biological tissues with the precise control and programmability of traditional robots, making them a focal point in the field of robotics. This paper reviews the latest research progress in bio-syncretic robots. Initially, we classify and introduce bio-syncretic robots from the perspective of structural design, which incorporates both biological and artificial materials. Subsequently, we provide a detailed discussion of their fabrication techniques and control methodologies. Finally, to facilitate broader applications of bio-syncretic robots, this paper explores their potential applications and future development prospects.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 4","pages":"Article 100263"},"PeriodicalIF":5.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416698","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}
Pub Date : 2025-08-26DOI: 10.1016/j.birob.2025.100257
Mingchao Deng , Ding Sun , Tiancheng Zhou , Yixin Gu , Zhongliang Jiang , Fengfeng Zhang , Lining Sun , Bo Lu
Open tumor resection is one of the most commonly used treatments for malignant liver tumors. The ability to accurately locate the liver tumor during the operation is the key to the success of the operation. Intraoperative liver tumor localization remains challenging due to tissue deformation and intraoperative imaging limitations. This paper proposes a dual-constraint framework that synergistically integrates liver surface deformation and vascular biomechanical modeling to resolve this problem. Liver surface registration captures global deformation using a fast finite-element model (18 s), while vascular topology matching refines internal tumor displacement by enforcing correspondence between preoperative and intraoperative vessel trees. This synergistic strategy leverages both external and internal anatomical cues to achieve robust localization. Evaluated on 13 clinical cases, our method achieved sub-millimeter tumor localization accuracy (1.68 0.22 mm). Compared to single-constraint methods (LTLS: 2.04 0.26 mm; LTBV: 2.23 0.31 mm), our approach reduced error by 24%–37% without increasing runtime. This clinically efficient method shows promise for improving intraoperative guidance during liver tumor ablation.
{"title":"Automated tumor localization via synergistic liver surface and vascular constraints deformation","authors":"Mingchao Deng , Ding Sun , Tiancheng Zhou , Yixin Gu , Zhongliang Jiang , Fengfeng Zhang , Lining Sun , Bo Lu","doi":"10.1016/j.birob.2025.100257","DOIUrl":"10.1016/j.birob.2025.100257","url":null,"abstract":"<div><div>Open tumor resection is one of the most commonly used treatments for malignant liver tumors. The ability to accurately locate the liver tumor during the operation is the key to the success of the operation. Intraoperative liver tumor localization remains challenging due to tissue deformation and intraoperative imaging limitations. This paper proposes a dual-constraint framework that synergistically integrates liver surface deformation and vascular biomechanical modeling to resolve this problem. Liver surface registration captures global deformation using a fast finite-element model (18<!--> <!-->s), while vascular topology matching refines internal tumor displacement by enforcing correspondence between preoperative and intraoperative vessel trees. This synergistic strategy leverages both external and internal anatomical cues to achieve robust localization. Evaluated on 13 clinical cases, our method achieved sub-millimeter tumor localization accuracy (1.68<!--> <span><math><mo>±</mo></math></span>\u0000 <!--> <!-->0.22 mm). Compared to single-constraint methods (LTLS: 2.04<!--> <span><math><mo>±</mo></math></span>\u0000 <!--> <!-->0.26 mm; LTBV: 2.23<!--> <span><math><mo>±</mo></math></span>\u0000 <!--> <!-->0.31 mm), our approach reduced error by 24%–37% without increasing runtime. This clinically efficient method shows promise for improving intraoperative guidance during liver tumor ablation.</div></div>","PeriodicalId":100184,"journal":{"name":"Biomimetic Intelligence and Robotics","volume":"5 4","pages":"Article 100257"},"PeriodicalIF":5.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416758","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}
Pub Date : 2025-08-20DOI: 10.1016/j.birob.2025.100258
Xin Li , Wenfu Xu , Zaiqiao Ye , Han Yuan
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.
{"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":"2025-08-20","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}
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.
{"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":"2025-08-13","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}
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