Pub Date : 2025-11-19DOI: 10.1126/scirobotics.aed1537
Robin R. Murphy
Robot assistants in Superman and The Fantastic Four: First Steps may not save the world, but they fulfill six different jobs.
在《超人》和《神奇四侠：第一步》中，机器人助手可能不会拯救世界，但他们可以完成六项不同的工作。
{"title":"The robots in Superman and The Fantastic Four: First Steps are as amazing as the superheroes","authors":"Robin R. Murphy","doi":"10.1126/scirobotics.aed1537","DOIUrl":"10.1126/scirobotics.aed1537","url":null,"abstract":"<div >Robot assistants in <i>Superman</i> and <i>The Fantastic Four: First Steps</i> may not save the world, but they fulfill six different jobs.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 108","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mechanical metamaterials with customized microstructures are increasingly shaping robotic design and functionality, enabling the integration of sensing, actuation, control, and computation within the robot body. This Review outlines how metamaterial design principles—mechanics-inspired architectures, shape-reconfigurable structures, and material-driven functionality—enhance adaptability and distributed intelligence in robotics. We also discuss how artificial intelligence supports metamaterial robotics in design, modeling, and control, advancing systems with complex sensory feedback, learning capability, and adaptive physical interactions. This Review aims to inspire the community to explore the transformative potential of metamaterial robotics, fostering innovations that bridge the gap between materials engineering and intelligent robotics.
{"title":"Metamaterial robotics","authors":"Xiaoyang Zheng, Yuhao Jiang, Mustafa Mete, Jingjing Li, Ikumu Watanabe, Takayuki Yamada, Jamie Paik","doi":"10.1126/scirobotics.adx1519","DOIUrl":"10.1126/scirobotics.adx1519","url":null,"abstract":"<div >Mechanical metamaterials with customized microstructures are increasingly shaping robotic design and functionality, enabling the integration of sensing, actuation, control, and computation within the robot body. This Review outlines how metamaterial design principles—mechanics-inspired architectures, shape-reconfigurable structures, and material-driven functionality—enhance adaptability and distributed intelligence in robotics. We also discuss how artificial intelligence supports metamaterial robotics in design, modeling, and control, advancing systems with complex sensory feedback, learning capability, and adaptive physical interactions. This Review aims to inspire the community to explore the transformative potential of metamaterial robotics, fostering innovations that bridge the gap between materials engineering and intelligent robotics.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 108","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1126/scirobotics.ads8652
Keaton L. Scherpereel, Matthew C. Gombolay, Max K. Shepherd, Carlos A. Carrasquillo, Omer T. Inan, Aaron J. Young
Data-driven methods have transformed our ability to assess and respond to human movement with wearable robots, promising real-world rehabilitation and augmentation benefits. However, the proliferation of data-driven methods, with the associated demand for increased personalization and performance, requires vast quantities of high-quality, device-specific data. Procuring these data is often intractable because of resource and personnel costs. We propose a framework that overcomes data scarcity by leveraging simulated sensors from biomechanical models to form a stepping-stone domain through which easily accessible data can be translated into data-limited domains. We developed and optimized a deep domain adaptation network that replaces costly, device-specific, labeled data with open-source datasets and unlabeled exoskeleton data. Using our network, we trained a hip and knee joint moment estimator with performance comparable to a best-case model trained with a complete, device-specific dataset [incurring only an 11 to 20%, 0.019 to 0.028 newton-meters per kilogram (Nm/kg) increase in error for a semisupervised model and 20 to 44%, 0.033 to 0.062 Nm/kg for an unsupervised model]. Our network significantly outperformed counterpart networks without domain adaptation (which incurred errors of 36 to 45% semisupervised and 50 to 60% unsupervised). Deploying our models in the real-time control loop of a hip/knee exoskeleton (N = 8) demonstrated estimator performance similar to offline results while augmenting user performance based on those estimated moments (9.5 to 14.6% metabolic cost reductions compared with no exoskeleton). Our framework enables researchers to train real-time deployable deep learning, task-agnostic models with limited or no access to labeled, device-specific data.
{"title":"Deep domain adaptation eliminates costly data required for task-agnostic wearable robotic control","authors":"Keaton L. Scherpereel, Matthew C. Gombolay, Max K. Shepherd, Carlos A. Carrasquillo, Omer T. Inan, Aaron J. Young","doi":"10.1126/scirobotics.ads8652","DOIUrl":"10.1126/scirobotics.ads8652","url":null,"abstract":"<div >Data-driven methods have transformed our ability to assess and respond to human movement with wearable robots, promising real-world rehabilitation and augmentation benefits. However, the proliferation of data-driven methods, with the associated demand for increased personalization and performance, requires vast quantities of high-quality, device-specific data. Procuring these data is often intractable because of resource and personnel costs. We propose a framework that overcomes data scarcity by leveraging simulated sensors from biomechanical models to form a stepping-stone domain through which easily accessible data can be translated into data-limited domains. We developed and optimized a deep domain adaptation network that replaces costly, device-specific, labeled data with open-source datasets and unlabeled exoskeleton data. Using our network, we trained a hip and knee joint moment estimator with performance comparable to a best-case model trained with a complete, device-specific dataset [incurring only an 11 to 20%, 0.019 to 0.028 newton-meters per kilogram (Nm/kg) increase in error for a semisupervised model and 20 to 44%, 0.033 to 0.062 Nm/kg for an unsupervised model]. Our network significantly outperformed counterpart networks without domain adaptation (which incurred errors of 36 to 45% semisupervised and 50 to 60% unsupervised). Deploying our models in the real-time control loop of a hip/knee exoskeleton (<i>N</i> = 8) demonstrated estimator performance similar to offline results while augmenting user performance based on those estimated moments (9.5 to 14.6% metabolic cost reductions compared with no exoskeleton). Our framework enables researchers to train real-time deployable deep learning, task-agnostic models with limited or no access to labeled, device-specific data.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 108","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-12DOI: 10.1126/scirobotics.adx3883
Steven Man, Sukjun Kim, Sarah Bergbreiter
Physical scaling laws predict that miniaturizing robotic mechanisms should enable exceptional robot performance in metrics such as speed and precision. Although these scaling laws have been explored in a variety of microsystems, the benefits and limitations of downscaling three-dimensional (3D) robotic mechanisms have yet to be assessed because of limitations in microscale 3D manufacturing. In this work, we used the Delta robot as a case study for these scaling laws. We present two sizes of 3D-printed Delta robots, the microDeltas, measuring 1.4 and 0.7 millimeters in height, which demonstrate state-of-the-art performance in both size and speed compared with previously reported Delta robots. Printing with two-photon polymerization and subsequent metallization enabled the miniaturization of these 3D robotic parallel mechanisms integrated with electrostatic actuators for achieving high bandwidths. The smallest microDelta was able to operate at more than 1000 hertz and achieved precisions of less than 1 micrometer by taking advantage of its small size. The microDelta’s relatively high output power was demonstrated with the launch of a small projectile, highlighting the utility of miniaturized robotic systems for applications ranging from manufacturing to haptics.
{"title":"The microDelta: Downscaling robot mechanisms enables ultrafast and high-precision movement","authors":"Steven Man, Sukjun Kim, Sarah Bergbreiter","doi":"10.1126/scirobotics.adx3883","DOIUrl":"10.1126/scirobotics.adx3883","url":null,"abstract":"<div >Physical scaling laws predict that miniaturizing robotic mechanisms should enable exceptional robot performance in metrics such as speed and precision. Although these scaling laws have been explored in a variety of microsystems, the benefits and limitations of downscaling three-dimensional (3D) robotic mechanisms have yet to be assessed because of limitations in microscale 3D manufacturing. In this work, we used the Delta robot as a case study for these scaling laws. We present two sizes of 3D-printed Delta robots, the microDeltas, measuring 1.4 and 0.7 millimeters in height, which demonstrate state-of-the-art performance in both size and speed compared with previously reported Delta robots. Printing with two-photon polymerization and subsequent metallization enabled the miniaturization of these 3D robotic parallel mechanisms integrated with electrostatic actuators for achieving high bandwidths. The smallest microDelta was able to operate at more than 1000 hertz and achieved precisions of less than 1 micrometer by taking advantage of its small size. The microDelta’s relatively high output power was demonstrated with the launch of a small projectile, highlighting the utility of miniaturized robotic systems for applications ranging from manufacturing to haptics.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 108","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145492745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-12DOI: 10.1126/scirobotics.adv7594
Kamil Dreczkowski, Pietro Vitiello, Vitalis Vosylius, Edward Johns
Humans are remarkably efficient at learning tasks from demonstrations, but today’s imitation learning methods for robot manipulation often require hundreds or thousands of demonstrations per task. We investigated two fundamental priors for improving learning efficiency: decomposing manipulation trajectories into sequential alignment and interaction phases and retrieval-based generalization. Through 3450 real-world rollouts, we systematically studied this decomposition. We compared different design choices for the alignment and interaction phases and examined generalization and scaling trends relative to today’s dominant paradigm of behavioral cloning with a single-phase monolithic policy. In the few-demonstrations-per-task regime (<10 demonstrations), decomposition achieved an order of magnitude of improvement in data efficiency over single-phase learning, with retrieval consistently outperforming behavioral cloning for both alignment and interaction. Building on these insights, we developed Multi-Task Trajectory Transfer (MT3), an imitation learning method based on decomposition and retrieval. MT3 learns everyday manipulation tasks from as little as a single demonstration each while also generalizing to previously unseen object instances. This efficiency enabled us to teach a robot 1000 distinct everyday tasks in under 24 hours of human demonstrator time. Through 2200 additional real-world rollouts, we reveal MT3’s capabilities and limitations across different task families.
{"title":"Learning a thousand tasks in a day","authors":"Kamil Dreczkowski, Pietro Vitiello, Vitalis Vosylius, Edward Johns","doi":"10.1126/scirobotics.adv7594","DOIUrl":"10.1126/scirobotics.adv7594","url":null,"abstract":"<div >Humans are remarkably efficient at learning tasks from demonstrations, but today’s imitation learning methods for robot manipulation often require hundreds or thousands of demonstrations per task. We investigated two fundamental priors for improving learning efficiency: decomposing manipulation trajectories into sequential alignment and interaction phases and retrieval-based generalization. Through 3450 real-world rollouts, we systematically studied this decomposition. We compared different design choices for the alignment and interaction phases and examined generalization and scaling trends relative to today’s dominant paradigm of behavioral cloning with a single-phase monolithic policy. In the few-demonstrations-per-task regime (<10 demonstrations), decomposition achieved an order of magnitude of improvement in data efficiency over single-phase learning, with retrieval consistently outperforming behavioral cloning for both alignment and interaction. Building on these insights, we developed Multi-Task Trajectory Transfer (MT3), an imitation learning method based on decomposition and retrieval. MT3 learns everyday manipulation tasks from as little as a single demonstration each while also generalizing to previously unseen object instances. This efficiency enabled us to teach a robot 1000 distinct everyday tasks in under 24 hours of human demonstrator time. Through 2200 additional real-world rollouts, we reveal MT3’s capabilities and limitations across different task families.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 108","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145492743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-29DOI: 10.1126/scirobotics.aeb2655
Seung Ryeol Lee, Hunsub Lim, Dongjun Shin
Team BeAGain’s development of the whole-body FES robotic bicycle and triumph at Cybathlon 2024 are presented.
BeAGain团队开发了全身FES机器人自行车，并在2024年Cybathlon上取得了胜利。
{"title":"Team BeAGain’s journey toward Cybathlon 2024 and holistic mobility with a robotic rehabilitation bicycle","authors":"Seung Ryeol Lee, Hunsub Lim, Dongjun Shin","doi":"10.1126/scirobotics.aeb2655","DOIUrl":"10.1126/scirobotics.aeb2655","url":null,"abstract":"<div >Team BeAGain’s development of the whole-body FES robotic bicycle and triumph at Cybathlon 2024 are presented.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 107","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Omnia bionic leg with a semipowered knee and ankle wins the Cybathlon 2024 leg prosthesis race","authors":"Benedetta Franconi, Andrea Cherubini, Alessia Sacchini, Samuele De Giuseppe, Alessandro Bunt, Andrea Berettoni, Andrea Modica, Nicolò Boccardo, Emanuele Gruppioni, Simone Traverso, Matteo Laffranchi","doi":"10.1126/scirobotics.aeb6485","DOIUrl":"10.1126/scirobotics.aeb6485","url":null,"abstract":"<div >Rehab Tech’s Omnia prosthesis excelled at Cybathlon, showcasing advanced lower-limb prostheses and user-centered innovation.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 107","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-29DOI: 10.1126/scirobotics.adu8015
Sihao Sun, Xuerui Wang, Dario Sanalitro, Antonio Franchi, Marco Tognon, Javier Alonso-Mora
Quadrotors can carry slung loads to hard-to-reach locations at high speed. Given that a single quadrotor has limited payload capacities, using a team of quadrotors to collaboratively manipulate the full pose of a heavy object is a scalable and promising solution. However, existing control algorithms for multilifting systems only enable low-speed and low-acceleration operations because of the complex dynamic coupling between quadrotors and the load, limiting their use in time-critical missions such as search and rescue. In this work, we present a solution to substantially enhance the agility of cable-suspended multilifting systems. Unlike traditional cascaded solutions, we introduce a trajectory-based framework that solves the whole-body kinodynamic motion planning problem online, accounting for the dynamic coupling effects and constraints between the quadrotors and the load. The planned trajectory is provided to the quadrotors as a reference in a receding-horizon fashion and is tracked by an onboard controller that observes and compensates for the cable tension. Real-world experiments demonstrate that our framework can achieve at least eight times greater acceleration than state-of-the-art methods to follow agile trajectories. Our method can even perform complex maneuvers such as flying through narrow passages at high speed. In addition, it exhibits high robustness against load uncertainties and wind disturbances and does not require adding any sensors to the load, demonstrating strong practicality.
{"title":"Agile and cooperative aerial manipulation of a cable-suspended load","authors":"Sihao Sun, Xuerui Wang, Dario Sanalitro, Antonio Franchi, Marco Tognon, Javier Alonso-Mora","doi":"10.1126/scirobotics.adu8015","DOIUrl":"10.1126/scirobotics.adu8015","url":null,"abstract":"<div >Quadrotors can carry slung loads to hard-to-reach locations at high speed. Given that a single quadrotor has limited payload capacities, using a team of quadrotors to collaboratively manipulate the full pose of a heavy object is a scalable and promising solution. However, existing control algorithms for multilifting systems only enable low-speed and low-acceleration operations because of the complex dynamic coupling between quadrotors and the load, limiting their use in time-critical missions such as search and rescue. In this work, we present a solution to substantially enhance the agility of cable-suspended multilifting systems. Unlike traditional cascaded solutions, we introduce a trajectory-based framework that solves the whole-body kinodynamic motion planning problem online, accounting for the dynamic coupling effects and constraints between the quadrotors and the load. The planned trajectory is provided to the quadrotors as a reference in a receding-horizon fashion and is tracked by an onboard controller that observes and compensates for the cable tension. Real-world experiments demonstrate that our framework can achieve at least eight times greater acceleration than state-of-the-art methods to follow agile trajectories. Our method can even perform complex maneuvers such as flying through narrow passages at high speed. In addition, it exhibits high robustness against load uncertainties and wind disturbances and does not require adding any sensors to the load, demonstrating strong practicality.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 107","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Shared control in assistive robotics: A Cybathlon-winning approach","authors":"Jörn Vogel, Mattias Atzenhofer, Gabriel Quere, Maged Iskandar, Miriam Welser, Felix Schiel, Sebastian Jung, Samuel Bustamante, Werner Friedl, Wout Boerdijk, Freek Stulp, Alin Albu-Schäffer, Annette Hagengruber","doi":"10.1126/scirobotics.aeb6725","DOIUrl":"10.1126/scirobotics.aeb6725","url":null,"abstract":"<div >Team EDAN and pilot Mattias Atzenhofer won the first Assistance Robot Race at Cybathlon 2024.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 107","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1126/scirobotics.adu4003
Lucio Pancaldi, Ece Özelçi, Mehdi Ali Gadiri, Julian Raub, Pascal John Mosimann, Mahmut Selman Sakar
Minimally invasive interventions performed inside brain vessels with the synergistic use of microcatheters pushed over guidewires have revolutionized the way aneurysms, strokes, arteriovenous malformations, brain tumors, and other cerebrovascular conditions are being treated. However, a substantial portion of the brain vasculature remains inaccessible because the conventional catheterization technique based on transmitting forces from the proximal to the distal end of the instruments imposes stringent constraints on their diameter and stiffness. Here, we overcame this mechanical barrier by microengineering ultraminiaturized magnetic microcatheters in the form of an inflatable flat tube, making them ultraflexible and capable of harnessing the kinetic energy of blood flow for endovascular navigation. We introduce a compact and versatile magnetic steering platform that is compatible with conventional biplane fluoroscope imaging and demonstrate safe and effortless navigation and tracking of hard-to-reach, distal, tortuous arteries that are as small as 180 micrometers in diameter with a curvature radius as small as 0.69 millimeters. Furthermore, we demonstrate the superselective infusion of contrast and embolic liquid agents, all in a porcine model. These results pave the way to reach, diagnose, and treat currently inaccessible distal arteries that may be at risk of bleeding or feeding a tumor. Our endovascular technology can also be used to selectively target tissues for drug or gene delivery from within the arteries, not only in the central and peripheral nervous systems but also in almost any other organ system, with improved accuracy, speed, and safety.
{"title":"Flow-driven magnetic microcatheter for superselective arterial embolization","authors":"Lucio Pancaldi, Ece Özelçi, Mehdi Ali Gadiri, Julian Raub, Pascal John Mosimann, Mahmut Selman Sakar","doi":"10.1126/scirobotics.adu4003","DOIUrl":"10.1126/scirobotics.adu4003","url":null,"abstract":"<div >Minimally invasive interventions performed inside brain vessels with the synergistic use of microcatheters pushed over guidewires have revolutionized the way aneurysms, strokes, arteriovenous malformations, brain tumors, and other cerebrovascular conditions are being treated. However, a substantial portion of the brain vasculature remains inaccessible because the conventional catheterization technique based on transmitting forces from the proximal to the distal end of the instruments imposes stringent constraints on their diameter and stiffness. Here, we overcame this mechanical barrier by microengineering ultraminiaturized magnetic microcatheters in the form of an inflatable flat tube, making them ultraflexible and capable of harnessing the kinetic energy of blood flow for endovascular navigation. We introduce a compact and versatile magnetic steering platform that is compatible with conventional biplane fluoroscope imaging and demonstrate safe and effortless navigation and tracking of hard-to-reach, distal, tortuous arteries that are as small as 180 micrometers in diameter with a curvature radius as small as 0.69 millimeters. Furthermore, we demonstrate the superselective infusion of contrast and embolic liquid agents, all in a porcine model. These results pave the way to reach, diagnose, and treat currently inaccessible distal arteries that may be at risk of bleeding or feeding a tumor. Our endovascular technology can also be used to selectively target tissues for drug or gene delivery from within the arteries, not only in the central and peripheral nervous systems but also in almost any other organ system, with improved accuracy, speed, and safety.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 107","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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