Science Robotics最新文献

英文中文

Robotic manipulation of human bipedalism reveals overlapping internal representations of space and time 机器人对人类两足行走的操纵揭示了空间和时间的重叠内部表征

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-11-26 DOI: 10.1126/scirobotics.adv0496

Paul Belzner, Patrick A. Forbes, Calvin Kuo, Jean-Sébastien Blouin

Effective control of bipedal postures relies on sensory inputs from the past, which encode dynamic changes in the spatial properties of our movement over time. To uncover how the spatial and temporal properties of an upright posture interact in the perception and control of standing balance, we implemented a robotic virtualization of human body dynamics to systematically alter inertia and viscosity as well as sensorimotor delays in 20 healthy participants. Inertia gains below one or negative viscosity gains led to larger postural oscillations and caused participants to exceed virtual balance limits, mimicking the disruptive effects of an additional 200-millisecond sensorimotor delay. When balancing without delays, participants adjusted their inertia gains to below one and viscosity gains to negative values to match the perception of balancing with an imposed delay. When delays were present, participants increased inertia gains above one and used positive viscosity gains to align their perception with baseline balance. Building on these findings, 10 naïve participants exhibited improved balance stability and reduced the number of instances they exceeded the limits when balancing with a 200-millisecond delay compensated by inertia gains above one and positive viscosity gains. These results underscore the importance of innovative robotic virtualizations of standing balance to reveal the interconnected representations of space and time that underlie the stable perception and control of bipedal balance. Robotic manipulation of body physics offers a transformative approach to understanding how the nervous system processes spatial information over time and could address clinical sensorimotor deficits associated with delays.

对两足姿势的有效控制依赖于过去的感官输入，这些输入编码了我们运动的空间特性随时间的动态变化。为了揭示直立姿势的空间和时间特性如何在站立平衡的感知和控制中相互作用，我们实施了人体动力学的机器人虚拟化，以系统地改变20名健康参与者的惯性、粘度和感觉运动延迟。惯性增益低于1或负粘度增益导致较大的姿势振荡，并导致参与者超过虚拟平衡极限，模仿额外200毫秒的感觉运动延迟的破坏性影响。当没有延迟的平衡时，参与者将他们的惯性增益调整到1以下，粘度增益调整到负值，以匹配与强加延迟的平衡的感知。当存在延迟时，参与者将惯性增益增加到1以上，并使用正粘度增益使他们的感知与基线平衡保持一致。在这些发现的基础上，10名naïve参与者表现出更好的平衡稳定性，并减少了他们在200毫秒的延迟平衡时超过限制的实例数量，这些延迟由大于1的惯性增益和正粘度增益补偿。这些结果强调了创新机器人站立平衡虚拟化的重要性，以揭示空间和时间的相互关联表征，这是两足平衡稳定感知和控制的基础。机器人对身体物理的操纵为理解神经系统如何随时间处理空间信息提供了一种变革性的方法，并可以解决与延迟相关的临床感觉运动缺陷。

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引用次数: 0

Robotic cross-pollination of genetically modified flowers 转基因花的机器人异花授粉。

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-11-19 DOI: 10.1126/scirobotics.aed6762

Melisa Yashinski

Engineered tomato plants produced flowers with visible stigmas that a robot could detect and pollinate faster than a human.

经过基因改造的番茄植株结出了带有可见柱头的花朵，机器人可以比人类更快地检测到这些花朵并进行授粉。

引用次数: 0

Erratum for the Research Article “A lightweight robotic leg prosthesis replicating the biomechanics of the knee, ankle, and toe joint” by M. Tran et al. M. Tran等人的研究文章“复制膝盖、脚踝和脚趾关节生物力学的轻型机械腿假体”的勘误。

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-11-19 DOI: 10.1126/scirobotics.aec6029

引用次数: 0

The robots in Superman and The Fantastic Four: First Steps are as amazing as the superheroes 《超人》和《神奇四侠：第一步》中的机器人和超级英雄一样令人惊叹

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-11-19 DOI: 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。

引用次数: 0

Metamaterial robotics 超材料的机器人

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-11-19 DOI: 10.1126/scirobotics.adx1519

Xiaoyang Zheng, Yuhao Jiang, Mustafa Mete, Jingjing Li, Ikumu Watanabe, Takayuki Yamada, Jamie Paik

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.

具有定制微结构的机械超材料越来越多地影响机器人的设计和功能，使传感、驱动、控制和计算在机器人体内集成。这篇综述概述了超材料设计原则——力学启发的结构、形状可重构结构和材料驱动的功能——如何增强机器人的适应性和分布式智能。我们还讨论了人工智能如何在设计、建模和控制方面支持超材料机器人，推进具有复杂感官反馈、学习能力和自适应物理交互的系统。本综述旨在激发社会探索超材料机器人的变革潜力，促进创新，弥合材料工程和智能机器人之间的差距。

引用次数: 0

Deep domain adaptation eliminates costly data required for task-agnostic wearable robotic control 深度域自适应消除了任务不可知可穿戴机器人控制所需的昂贵数据

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-11-19 DOI: 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.

数据驱动的方法改变了我们用可穿戴机器人评估和响应人类运动的能力，为现实世界的康复和增强带来了希望。然而，数据驱动方法的激增，以及对个性化和性能提高的相关需求，需要大量高质量的、特定于设备的数据。由于资源和人员成本的原因，获取这些数据通常是棘手的。我们提出了一个框架，通过利用来自生物力学模型的模拟传感器来形成一个踏脚石域，通过这个踏脚石域，容易访问的数据可以转化为数据有限的域，从而克服数据稀缺性。我们开发并优化了一个深度域适应网络，用开源数据集和未标记的外骨骼数据取代昂贵的、特定于设备的标记数据。使用我们的网络，我们训练了一个髋关节和膝关节力矩估计器，其性能与使用完整的设备特定数据集训练的最佳情况模型相当[对于半监督模型，误差仅增加11%至20%，0.019至0.028牛顿-米/千克（Nm/kg），对于无监督模型，误差仅增加20%至44%，0.033至0.062 Nm/千克]。我们的网络明显优于没有域适应的对应网络（半监督和无监督的误差率分别为36%到45%和50%到60%）。将我们的模型部署到髋关节/膝关节外骨骼（N = 8）的实时控制回路中，证明了估计器的性能与离线结果相似，同时基于这些估计力矩增强了用户的性能（与没有外骨骼相比，代谢成本降低了9.5%至14.6%）。我们的框架使研究人员能够训练实时部署的深度学习，任务无关模型，限制或无法访问标记的设备特定数据。

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引用次数: 0

The microDelta: Downscaling robot mechanisms enables ultrafast and high-precision movement microDelta：缩小机器人机构，实现超快速和高精度的运动

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-11-12 DOI: 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.

物理缩放定律预测，小型化机器人机构应该使机器人在速度和精度等指标上表现出色。尽管这些缩放规律已经在各种微系统中进行了探索，但由于微尺度3D制造的局限性，缩小三维（3D）机器人机构的好处和局限性尚未得到评估。在这项工作中，我们使用Delta机器人作为这些缩放定律的案例研究。我们展示了两种尺寸的3d打印德尔塔机器人，微德尔塔，高度为1.4毫米和0.7毫米，与之前报道的德尔塔机器人相比，它们在尺寸和速度上都表现出了最先进的性能。双光子聚合打印和随后的金属化使得这些3D机器人并联机构小型化，并与静电致动器集成，以实现高带宽。最小的microDelta能够在超过1000赫兹的频率下工作，并利用其小尺寸的优势实现小于1微米的精度。microDelta相对较高的输出功率通过发射一枚小型弹丸进行了演示，突出了小型化机器人系统在从制造到触觉等应用领域的实用性。

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引用次数: 0

Learning a thousand tasks in a day 一天学习一千项任务

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-11-12 DOI: 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.

人类在从演示中学习任务方面非常有效，但今天用于机器人操作的模仿学习方法通常需要每个任务数百或数千个演示。我们研究了提高学习效率的两个基本前提：将操作轨迹分解为顺序对齐和交互阶段以及基于检索的泛化。通过3450次实际部署，我们系统地研究了这种分解。我们比较了对齐和交互阶段的不同设计选择，并研究了与当今主导的单相单片策略行为克隆范式相关的泛化和扩展趋势。在每个任务的少量演示机制（<；10个演示）中，分解在数据效率方面比单相学习取得了数量级的改进，检索在对齐和交互方面始终优于行为克隆。基于这些见解，我们开发了多任务轨迹迁移（MT3），这是一种基于分解和检索的模仿学习方法。MT3学习日常操作任务，从少到单个演示，同时也推广到以前未见过的对象实例。这种效率使我们能够在不到24小时的人类示范时间内教会机器人1000种不同的日常任务。通过2200次实际部署，我们揭示了MT3在不同任务族中的功能和局限性。

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引用次数: 0

Team BeAGain’s journey toward Cybathlon 2024 and holistic mobility with a robotic rehabilitation bicycle BeAGain团队的2024年Cybathlon之旅以及机器人康复自行车的整体移动性。

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-10-29 DOI: 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。

引用次数: 0

The Omnia bionic leg with a semipowered knee and ankle wins the Cybathlon 2024 leg prosthesis race 拥有半动力膝盖和脚踝的Omnia仿生腿赢得了2024年Cybathlon假肢比赛。

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-10-29 DOI: 10.1126/scirobotics.aeb6485

Benedetta Franconi, Andrea Cherubini, Alessia Sacchini, Samuele De Giuseppe, Alessandro Bunt, Andrea Berettoni, Andrea Modica, Nicolò Boccardo, Emanuele Gruppioni, Simone Traverso, Matteo Laffranchi

Rehab Tech’s Omnia prosthesis excelled at Cybathlon, showcasing advanced lower-limb prostheses and user-centered innovation.

康复科技公司的Omnia假肢在Cybathlon上表现出色，展示了先进的下肢假肢和以用户为中心的创新。

引用次数: 0

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