Science Robotics最新文献

英文中文

Learning contact-rich whole-body manipulation with example-guided reinforcement learning 学习接触丰富的全身操作与例子引导强化学习

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-20 DOI: 10.1126/scirobotics.ads6790

Jose A. Barreiros, Aykut Özgün Önol, Mengchao Zhang, Sam Creasey, Aimee Goncalves, Andrew Beaulieu, Aditya Bhat, Kate M. Tsui, Alex Alspach

Humans use diverse skills and strategies to effectively manipulate various objects, ranging from dexterous in-hand manipulation (fine motor skills) to complex whole-body manipulation (gross motor skills). The latter involves full-body engagement and extensive contact with various body parts beyond just the hands, where the compliance of our skin and muscles plays a crucial role in increasing contact stability and mitigating uncertainty. For robots, synthesizing these contact-rich behaviors has fundamental challenges because of the rapidly growing combinatorics inherent to this amount of contact, making explicit reasoning about all contact interactions intractable. We explore the use of example-guided reinforcement learning to generate robust whole-body skills for the manipulation of large and unwieldy objects. Our method’s effectiveness is demonstrated on Toyota Research Institute’s Punyo robot, a humanoid upper body with highly deformable, pressure-sensing skin. Training was conducted in simulation with only a single example motion per object manipulation task, and policies were easily transferred to hardware owing to domain randomization and the robot’s compliance. The resulting agent can manipulate various everyday objects, such as a water jug and large boxes, in a similar fashion to the example motion. In addition, we show blind dexterous whole-body manipulation, relying solely on proprioceptive and tactile feedback without object pose tracking. Our analysis highlights the critical role of compliance in facilitating whole-body manipulation with humanoid robots.

人类使用不同的技能和策略来有效地操纵各种物体，从灵巧的手部操作（精细运动技能）到复杂的全身操作（大运动技能）。后者涉及全身接触和广泛接触身体的各个部位，而不仅仅是手，其中我们的皮肤和肌肉的顺应性在增加接触稳定性和减轻不确定性方面起着至关重要的作用。对于机器人来说，综合这些接触丰富的行为具有根本性的挑战，因为这种接触量所固有的快速增长的组合学，使得对所有接触相互作用的明确推理变得难以处理。我们探索使用示例引导强化学习来生成强大的全身技能，以操纵大型和笨重的物体。我们的方法的有效性在丰田研究所的Punyo机器人上得到了证明，Punyo机器人是一个人形的上半身，具有高度可变形的压力感应皮肤。训练是在模拟中进行的，每个目标操作任务只有一个例子运动，并且由于领域随机化和机器人的顺应性，策略很容易转移到硬件。由此产生的智能体可以以类似于示例运动的方式操作各种日常物品，例如水壶和大盒子。此外，我们展示了盲灵巧的全身操作，仅仅依靠本体感觉和触觉反馈，而不需要物体姿态跟踪。我们的分析强调了顺应性在促进人形机器人的全身操作中的关键作用。

{"title":"Learning contact-rich whole-body manipulation with example-guided reinforcement learning","authors":"Jose A. Barreiros, Aykut Özgün Önol, Mengchao Zhang, Sam Creasey, Aimee Goncalves, Andrew Beaulieu, Aditya Bhat, Kate M. Tsui, Alex Alspach","doi":"10.1126/scirobotics.ads6790","DOIUrl":"10.1126/scirobotics.ads6790","url":null,"abstract":"<div >Humans use diverse skills and strategies to effectively manipulate various objects, ranging from dexterous in-hand manipulation (fine motor skills) to complex whole-body manipulation (gross motor skills). The latter involves full-body engagement and extensive contact with various body parts beyond just the hands, where the compliance of our skin and muscles plays a crucial role in increasing contact stability and mitigating uncertainty. For robots, synthesizing these contact-rich behaviors has fundamental challenges because of the rapidly growing combinatorics inherent to this amount of contact, making explicit reasoning about all contact interactions intractable. We explore the use of example-guided reinforcement learning to generate robust whole-body skills for the manipulation of large and unwieldy objects. Our method’s effectiveness is demonstrated on Toyota Research Institute’s Punyo robot, a humanoid upper body with highly deformable, pressure-sensing skin. Training was conducted in simulation with only a single example motion per object manipulation task, and policies were easily transferred to hardware owing to domain randomization and the robot’s compliance. The resulting agent can manipulate various everyday objects, such as a water jug and large boxes, in a similar fashion to the example motion. In addition, we show blind dexterous whole-body manipulation, relying solely on proprioceptive and tactile feedback without object pose tracking. Our analysis highlights the critical role of compliance in facilitating whole-body manipulation with humanoid robots.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 105","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881594","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}

引用次数: 0

Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning 通过人在环强化学习的精确和灵巧的机器人操作

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-20 DOI: 10.1126/scirobotics.ads5033

Jianlan Luo, Charles Xu, Jeffrey Wu, Sergey Levine

Robotic manipulation remains one of the most difficult challenges in robotics, with approaches ranging from classical model-based control to modern imitation learning. Although these methods have enabled substantial progress, they often require extensive manual design, struggle with performance, and demand large-scale data collection. These limitations hinder their real-world deployment at scale, where reliability, speed, and robustness are essential. Reinforcement learning (RL) offers a powerful alternative by enabling robots to autonomously acquire complex manipulation skills through interaction. However, realizing the full potential of RL in the real world remains challenging because of issues of sample efficiency and safety. We present a human-in-the-loop, vision-based RL system that achieved strong performance on a wide range of dexterous manipulation tasks, including precise assembly, dynamic manipulation, and dual-arm coordination. These tasks reflect realistic industrial tolerances, with small but critical variations in initial object placements that demand sophisticated reactive control. Our method integrates demonstrations, human corrections, sample-efficient RL algorithms, and system-level design to directly learn RL policies in the real world. Within 1 to 2.5 hours of real-world training, our approach outperformed other baselines by improving task success by 2×, achieving near-perfect success rates, and executing 1.8× faster on average. Through extensive experiments and analysis, our results suggest that RL can learn a wide range of complex vision-based manipulation policies directly in the real world within practical training times. We hope that this work will inspire a new generation of learned robotic manipulation techniques, benefiting both industrial applications and research advancements.

机器人操作仍然是机器人技术中最困难的挑战之一，其方法从经典的基于模型的控制到现代模仿学习。尽管这些方法已经取得了实质性的进展，但它们通常需要大量的手工设计，在性能方面存在问题，并且需要大规模的数据收集。这些限制阻碍了它们在现实世界中的大规模部署，而可靠性、速度和健壮性是必不可少的。强化学习（RL）提供了一个强大的替代方案，使机器人能够通过交互自主地获得复杂的操作技能。然而，由于样本效率和安全性问题，在现实世界中实现强化学习的全部潜力仍然具有挑战性。我们提出了一个基于视觉的人在环强化学习系统，该系统在广泛的灵巧操作任务中取得了强大的性能，包括精确装配、动态操作和双臂协调。这些任务反映了现实的工业公差，初始物体放置位置的微小但关键的变化需要复杂的反应控制。我们的方法集成了演示、人工校正、样本高效强化学习算法和系统级设计，以直接学习现实世界中的强化学习策略。在1到2.5小时的真实世界训练中，我们的方法通过将任务成功率提高2倍，实现近乎完美的成功率，平均执行速度提高1.8倍，优于其他基准。通过大量的实验和分析，我们的研究结果表明，强化学习可以在实际训练时间内直接在现实世界中学习各种复杂的基于视觉的操作策略。我们希望这项工作将激发新一代的学习机器人操作技术，有利于工业应用和研究进展。

{"title":"Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning","authors":"Jianlan Luo, Charles Xu, Jeffrey Wu, Sergey Levine","doi":"10.1126/scirobotics.ads5033","DOIUrl":"10.1126/scirobotics.ads5033","url":null,"abstract":"<div >Robotic manipulation remains one of the most difficult challenges in robotics, with approaches ranging from classical model-based control to modern imitation learning. Although these methods have enabled substantial progress, they often require extensive manual design, struggle with performance, and demand large-scale data collection. These limitations hinder their real-world deployment at scale, where reliability, speed, and robustness are essential. Reinforcement learning (RL) offers a powerful alternative by enabling robots to autonomously acquire complex manipulation skills through interaction. However, realizing the full potential of RL in the real world remains challenging because of issues of sample efficiency and safety. We present a human-in-the-loop, vision-based RL system that achieved strong performance on a wide range of dexterous manipulation tasks, including precise assembly, dynamic manipulation, and dual-arm coordination. These tasks reflect realistic industrial tolerances, with small but critical variations in initial object placements that demand sophisticated reactive control. Our method integrates demonstrations, human corrections, sample-efficient RL algorithms, and system-level design to directly learn RL policies in the real world. Within 1 to 2.5 hours of real-world training, our approach outperformed other baselines by improving task success by 2×, achieving near-perfect success rates, and executing 1.8× faster on average. Through extensive experiments and analysis, our results suggest that RL can learn a wide range of complex vision-based manipulation policies directly in the real world within practical training times. We hope that this work will inspire a new generation of learned robotic manipulation techniques, benefiting both industrial applications and research advancements.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 105","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881604","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}

引用次数: 0

Robots in science fiction unload assumptions about freight logistics 科幻小说中的机器人卸下了对货运物流的假设

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-13 DOI: 10.1126/scirobotics.aea7372

Robin R. Murphy

Science fiction argues for specialized robots, not general-purpose humanoid robots, for unloading of cargo and parcels.

科幻小说主张使用专门的机器人，而不是通用的人形机器人来卸载货物和包裹。

引用次数: 0

Robotically steerable guidewires—Current trends and future directions 机器人导向导线——当前趋势和未来方向

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-13 DOI: 10.1126/scirobotics.adt7461

Revanth Konda, Timothy A. Brumfiel, Zachary L. Bercu, Jonathan A. Grossberg, Jaydev P. Desai

Research on robotically steerable guidewires has surged in the past decade because of their potential in addressing difficulties related to endovascular interventions. These microscale devices exhibit unique challenges in design, fabrication, and control, not necessarily present in mesoscale continuum robots such as robotic catheters and endoscopes. Existing literature on surgical robots mainly addresses advancements in robotic surgery with a focus on current trends in specific clinical procedures. Our article aims to bridge this gap by reviewing current clinical practices in endovascular interventions, highlighting the clinical motivations for the development of robotically steerable guidewires, and detailing the current advancements and future prospects in topics related to these devices.

机器人导向导丝的研究在过去十年中蓬勃发展，因为它们在解决与血管内介入相关的困难方面具有潜力。这些微尺度设备在设计、制造和控制方面面临着独特的挑战，而在中尺度连续体机器人（如机器人导尿管和内窥镜）中并不一定存在。关于手术机器人的现有文献主要涉及机器人手术的进展，重点关注特定临床手术的当前趋势。本文旨在通过回顾当前血管内介入的临床实践，强调开发机器人导向导丝的临床动机，并详细介绍与这些设备相关的主题的当前进展和未来前景，来弥合这一差距。

引用次数: 0

Plasticized electrohydraulic robot autopilots in the deep sea 塑化的电液机器人在深海中自动驾驶

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-13 DOI: 10.1126/scirobotics.adt8054

Guorui Li, Peng Shen, Tuck-Whye Wong, Mingyu Liu, Zhenxiang Sun, Xinyu Liu, Yongzai Chen, Xianghan Wang, Hao Zhang, Bingxu Hu, Deli Chen, Zhihan Zhang, Chao Zhang, Rongchen Wang, Wenhao Zhang, Shuai Nie, Xinyue Zhang, Jie-Wei Wong, Haofei Zhou, Wenbo Li, Hao Wang, Qian Zhang, Shenlong Wang, Zhiwen Yu, Hai Li, Hongyu Zhao, Qingyun Zeng, Shiping Wang, Zhilong Huang, Cong Ye, A-Man Zhang, Tiefeng Li

Soft robots, with their compliant bodies, minimal environmental disturbance, and ability to withstand ambient pressures, offer promising solutions for deep-sea exploration. However, a common challenge of stiffening in soft materials impairs their effective actuation in harsh conditions. In this work, we integrated a liquid dielectric plasticizer within an electrohydraulic soft robot, serving dual critical functions as a softening agent to maintain the softness of the polymer shell and an electrohydraulic fluid for efficient actuation. In addition, by using the surrounding seawater as alternating electrodes, we prevented charge retention in dielectric layers, enabling sustained actuation performance. Field tests at depths of ~1360, 3176, and ~4071 meters confirmed the robot’s ability to sense the environment, navigate complex trajectories, and withstand unsteady disturbances. Our work offers a generalized and straightforward framework for developing soft materials tailored for deep-sea applications, paving the way for soft robots to execute real-world missions.

软体机器人具有柔顺的身体、最小的环境干扰和承受环境压力的能力，为深海勘探提供了有前途的解决方案。然而，软质材料的一个共同挑战是硬化，这削弱了它们在恶劣条件下的有效驱动。在这项工作中，我们将液体介质增塑剂集成到电液软机器人中，作为柔软剂保持聚合物外壳的柔软性，并作为电液流体有效驱动，具有双重关键功能。此外，通过使用周围的海水作为交替电极，我们防止了介电层中的电荷保留，从而实现了持续的驱动性能。在~1360、3176和~4071米深度的现场测试证实了机器人感知环境、导航复杂轨迹和承受不稳定干扰的能力。我们的工作为开发适合深海应用的软材料提供了一个通用而直接的框架，为软机器人执行现实世界的任务铺平了道路。

{"title":"Plasticized electrohydraulic robot autopilots in the deep sea","authors":"Guorui Li, Peng Shen, Tuck-Whye Wong, Mingyu Liu, Zhenxiang Sun, Xinyu Liu, Yongzai Chen, Xianghan Wang, Hao Zhang, Bingxu Hu, Deli Chen, Zhihan Zhang, Chao Zhang, Rongchen Wang, Wenhao Zhang, Shuai Nie, Xinyue Zhang, Jie-Wei Wong, Haofei Zhou, Wenbo Li, Hao Wang, Qian Zhang, Shenlong Wang, Zhiwen Yu, Hai Li, Hongyu Zhao, Qingyun Zeng, Shiping Wang, Zhilong Huang, Cong Ye, A-Man Zhang, Tiefeng Li","doi":"10.1126/scirobotics.adt8054","DOIUrl":"10.1126/scirobotics.adt8054","url":null,"abstract":"<div >Soft robots, with their compliant bodies, minimal environmental disturbance, and ability to withstand ambient pressures, offer promising solutions for deep-sea exploration. However, a common challenge of stiffening in soft materials impairs their effective actuation in harsh conditions. In this work, we integrated a liquid dielectric plasticizer within an electrohydraulic soft robot, serving dual critical functions as a softening agent to maintain the softness of the polymer shell and an electrohydraulic fluid for efficient actuation. In addition, by using the surrounding seawater as alternating electrodes, we prevented charge retention in dielectric layers, enabling sustained actuation performance. Field tests at depths of ~1360, 3176, and ~4071 meters confirmed the robot’s ability to sense the environment, navigate complex trajectories, and withstand unsteady disturbances. Our work offers a generalized and straightforward framework for developing soft materials tailored for deep-sea applications, paving the way for soft robots to execute real-world missions.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 105","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833404","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}

引用次数: 0

Cooperative robotic exploration of a planetary skylight surface and lava cave 行星天窗表面和熔岩洞的合作机器人探索

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-13 DOI: 10.1126/scirobotics.adj9699

Raúl Domínguez, Carlos Pérez-del-Pulgar, Gonzalo J. Paz-Delgado, Fabio Polisano, Jonathan Babel, Thierry Germa, Iulia Dragomir, Valérie Ciarletti, Anne-Claire Berthet, Leon Cedric Danter, Frank Kirchner

Exploration of lava caves on the surface of planetary bodies near Earth is of high importance for scientific research and space exploration. The natural shielding that these caves offer against radiation and small meteorites makes them well suited for preserving exobiological signatures and protecting human-made facilities. The use of a robot team arises as the safest and most cost-efficient way to explore extraterrestrial lava caves because they are difficult to access. Although the approach has been demonstrated in similar scenarios on Earth, its adaptation to space conditions needs further research. Here, we define a lava cave exploration mission concept, including four mission phases that are performed by a heterogeneous team of three robots equipped with the required hardware and software. This mission concept was validated in a relevant scenario, a lava cave on Lanzarote island (Spain), where the team of robots was able to build a three-dimensional model of the surrounding area and skylight, introducing a scout rover through rappelling and exploring the inner part of the cave. The results obtained demonstrate the proposed mission concept’s feasibility, including three next-generation planetary exploration rovers that were coordinated to obtain meaningful information about the lava cave’s external and internal morphology.

探测近地行星体表面的熔岩洞对科学研究和空间探索具有重要意义。这些洞穴对辐射和小陨石的天然屏蔽使它们非常适合保存外星生物特征和保护人造设施。使用机器人团队是探索外星熔岩洞穴最安全、最具成本效益的方式，因为它们很难进入。虽然这种方法已经在地球上类似的场景中得到了证明，但它对太空条件的适应性还需要进一步的研究。在这里，我们定义了一个熔岩洞穴勘探任务概念，包括四个任务阶段，由三个配备所需硬件和软件的机器人组成的异构团队执行。这个任务概念在一个相关的场景中得到了验证，在兰萨罗特岛（西班牙）的熔岩洞中，机器人团队能够建立周围区域和天窗的三维模型，通过绳索下降引入侦察漫游者并探索洞穴的内部。所获得的结果证明了所提出的任务概念的可行性，包括三个下一代行星探测漫游者，它们相互协调，以获得有关熔岩洞外部和内部形态的有意义的信息。

{"title":"Cooperative robotic exploration of a planetary skylight surface and lava cave","authors":"Raúl Domínguez, Carlos Pérez-del-Pulgar, Gonzalo J. Paz-Delgado, Fabio Polisano, Jonathan Babel, Thierry Germa, Iulia Dragomir, Valérie Ciarletti, Anne-Claire Berthet, Leon Cedric Danter, Frank Kirchner","doi":"10.1126/scirobotics.adj9699","DOIUrl":"10.1126/scirobotics.adj9699","url":null,"abstract":"<div >Exploration of lava caves on the surface of planetary bodies near Earth is of high importance for scientific research and space exploration. The natural shielding that these caves offer against radiation and small meteorites makes them well suited for preserving exobiological signatures and protecting human-made facilities. The use of a robot team arises as the safest and most cost-efficient way to explore extraterrestrial lava caves because they are difficult to access. Although the approach has been demonstrated in similar scenarios on Earth, its adaptation to space conditions needs further research. Here, we define a lava cave exploration mission concept, including four mission phases that are performed by a heterogeneous team of three robots equipped with the required hardware and software. This mission concept was validated in a relevant scenario, a lava cave on Lanzarote island (Spain), where the team of robots was able to build a three-dimensional model of the surrounding area and skylight, introducing a scout rover through rappelling and exploring the inner part of the cave. The results obtained demonstrate the proposed mission concept’s feasibility, including three next-generation planetary exploration rovers that were coordinated to obtain meaningful information about the lava cave’s external and internal morphology.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 105","pages":""},"PeriodicalIF":27.5,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/scirobotics.adj9699","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI in therapeutic and assistive exoskeletons and exosuits: Influences on performance and autonomy 人工智能在治疗和辅助外骨骼和外骨骼：对性能和自主性的影响

IF 25 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-07-30 DOI: 10.1126/scirobotics.adt7329

Herman van der Kooij, Edwin H. F. van Asseldonk, Massimo Sartori, Chiara Basla, Adrian Esser, Robert Riener

Therapeutic and assistive exoskeletons and exosuits show promise in both clinical and real-world settings. Improving their autonomy can enhance usability, effectiveness, and cost efficiency. This Review presents a generic control framework for autonomous operation of upper and lower limb devices and reviews current advancements and future directions. We highlight how data-driven machine learning aids in intention recognition, synchronization, patient assessment, and task-agnostic control. In addition, we discuss how reinforcement learning optimizes control policies through digital human twins and how generative AI supports therapy planning and patient engagement. Richer patient-specific data and more accurate digital twins are needed for clinical validation and widespread deployment.

治疗性和辅助性外骨骼和外骨骼在临床和现实环境中都显示出前景。提高它们的自主性可以增强可用性、有效性和成本效率。本文介绍了一种用于上肢和下肢设备自主操作的通用控制框架，并综述了目前的进展和未来的发展方向。我们强调了数据驱动的机器学习在意图识别、同步、患者评估和任务不可知控制方面的帮助。此外，我们还讨论了强化学习如何通过数字人类双胞胎优化控制策略，以及生成式人工智能如何支持治疗计划和患者参与。临床验证和广泛部署需要更丰富的患者特定数据和更准确的数字双胞胎。

引用次数: 0

The grand challenges of learning medical robot autonomy 学习医疗机器人自主性的巨大挑战

IF 25 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-07-30 DOI: 10.1126/scirobotics.adz8279

Pierre E. Dupont, Alperen Degirmenci

Most medical robots depend on human operators for sensing, decision-making, and action during procedures. Future progress depends on enabling robots to take on these capabilities. Although learning-based approaches provide remarkable promise toward achieving this goal, notable challenges must be addressed to unlock these robots’ full potential in clinical settings.

大多数医疗机器人在操作过程中依赖人类操作员进行感知、决策和行动。未来的进步取决于让机器人具备这些能力。尽管基于学习的方法为实现这一目标提供了显著的希望，但要在临床环境中释放这些机器人的全部潜力，必须解决显著的挑战。

引用次数: 0

AI search, physician removal: Bronchoscopy robot bridges collaboration in foreign body aspiration 人工智能搜索，医生移除：支气管镜机器人在异物吸入方面的合作桥梁

IF 25 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-07-30 DOI: 10.1126/scirobotics.adt5338

Lilu Liu, Jingyu Zhang, Fei Wang, Jiyu Yu, Yuxiang Cui, Zhibin Li, Jian Hu, Rong Xiong, Haojian Lu, Yue Wang

Bronchial foreign body aspiration is a life-threatening condition with a high incidence across diverse populations, requiring urgent diagnosis and treatment. However, the limited availability of skilled practitioners and advanced medical equipment in community clinics and underdeveloped regions underscores the broader challenges in emergency care. Here, we present a cost-effective robotic bronchoscope capable of computed tomography (CT)–free, artificial intelligence (AI)–driven foreign body search and doctor-collaborated removal over long distances via fifth-generation (5G) communication. The system is built around a low-cost (<5000 USD), portable (<2 kilograms) bronchoscope robotic platform equipped with a 3.3-millimeter-diameter catheter and 1-millimeter biopsy forceps designed for safe pulmonary search and foreign body removal. Our AI algorithm, which integrates classical data structures with modern machine learning techniques, enables thorough CT-free lung coverage. The tree structure is leveraged to memorize a compact exploration process and guide the decision-making. Both virtual and physical simulations demonstrate the system’s effective autonomous foreign body search, minimizing bronchial wall contact to reduce patient discomfort. In a remote procedure, a physician in Hangzhou successfully retrieved a foreign body from a live pig located 1500 kilometers away in Chengdu using 5G communication, highlighting effective collaboration of AI, robotics, and human experts. We anticipate that this 5G-enabled, low-cost, AI expert–collaborated robotic platform has notable potential to reduce medical disparities, enhance emergency care, improve patient outcomes, decrease physician workload, and streamline medical procedures through the automation of routine tasks.

支气管异物吸入是一种危及生命的疾病，在不同人群中发病率很高，需要紧急诊断和治疗。然而，在社区诊所和欠发达地区，熟练的从业人员和先进的医疗设备有限，这凸显了急诊护理面临的更广泛挑战。在这里，我们提出了一种具有成本效益的机器人支气管镜，能够通过第五代（5G）通信进行无计算机断层扫描（CT），人工智能（AI）驱动的异物搜索和医生合作的长距离清除。该系统是围绕一个低成本（5000美元）、便携式（2公斤）支气管镜机器人平台构建的，该平台配备了直径3.3毫米的导管和1毫米的活检钳，用于安全的肺部搜索和异物清除。我们的人工智能算法将经典数据结构与现代机器学习技术相结合，可以实现彻底的无ct肺部覆盖。利用树形结构来记忆紧凑的探索过程并指导决策。虚拟和物理模拟都证明了该系统有效的自主异物搜索，最大限度地减少了支气管壁接触，减少了患者的不适。在一次远程手术中，杭州的一名医生利用5G通信成功地从1500公里外的成都的一头活猪身上取出了异物，这突显了人工智能、机器人和人类专家的有效协作。我们预计，这种支持5g、低成本、人工智能专家协作的机器人平台在缩小医疗差距、加强紧急护理、改善患者治疗效果、减少医生工作量以及通过日常任务自动化简化医疗程序方面具有显著潜力。

{"title":"AI search, physician removal: Bronchoscopy robot bridges collaboration in foreign body aspiration","authors":"Lilu Liu, Jingyu Zhang, Fei Wang, Jiyu Yu, Yuxiang Cui, Zhibin Li, Jian Hu, Rong Xiong, Haojian Lu, Yue Wang","doi":"10.1126/scirobotics.adt5338","DOIUrl":"https://doi.org/10.1126/scirobotics.adt5338","url":null,"abstract":"Bronchial foreign body aspiration is a life-threatening condition with a high incidence across diverse populations, requiring urgent diagnosis and treatment. However, the limited availability of skilled practitioners and advanced medical equipment in community clinics and underdeveloped regions underscores the broader challenges in emergency care. Here, we present a cost-effective robotic bronchoscope capable of computed tomography (CT)–free, artificial intelligence (AI)–driven foreign body search and doctor-collaborated removal over long distances via fifth-generation (5G) communication. The system is built around a low-cost (<5000 USD), portable (<2 kilograms) bronchoscope robotic platform equipped with a 3.3-millimeter-diameter catheter and 1-millimeter biopsy forceps designed for safe pulmonary search and foreign body removal. Our AI algorithm, which integrates classical data structures with modern machine learning techniques, enables thorough CT-free lung coverage. The tree structure is leveraged to memorize a compact exploration process and guide the decision-making. Both virtual and physical simulations demonstrate the system’s effective autonomous foreign body search, minimizing bronchial wall contact to reduce patient discomfort. In a remote procedure, a physician in Hangzhou successfully retrieved a foreign body from a live pig located 1500 kilometers away in Chengdu using 5G communication, highlighting effective collaboration of AI, robotics, and human experts. We anticipate that this 5G-enabled, low-cost, AI expert–collaborated robotic platform has notable potential to reduce medical disparities, enhance emergency care, improve patient outcomes, decrease physician workload, and streamline medical procedures through the automation of routine tasks.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"715 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144737523","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}

引用次数: 0

Medical robots learn to be autonomous. 医疗机器人学会了自主。

IF 25 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-07-30 DOI: 10.1126/scirobotics.adz8291

Pierre E Dupont

Numerous challenges in medicine could be addressed by harnessing autonomy and artificial intelligence in medical robots.

利用医疗机器人的自主性和人工智能可以解决医学领域的许多挑战。

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Science Robotics

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀