Science Robotics最新文献

英文中文

Robotic reading companions can mitigate oral reading anxiety in children 机器人阅读同伴可以减轻儿童的口语阅读焦虑

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-09-10 DOI: 10.1126/scirobotics.adu5771

Lauren L. Wright, Pooja Vegesna, Joseph E. Michaelis, Bilge Mutlu, Sarah Sebo

Reading fluency is a vital building block for developing literacy, yet the best way to practice fluency—reading aloud—can cause anxiety severe enough to inhibit literacy development in ways that can have an adverse effect on students through adulthood. One promising intervention to mitigate oral reading anxiety is to have children read aloud to a robot. Although observations in prior work have suggested that people likely feel more comfortable in the presence of a robot instead of a human, few studies have empirically demonstrated that people feel less anxious performing in front of a robot compared with a human or used objective physiological indicators to identify decreased anxiety. To investigate whether a robotic reading companion could reduce reading anxiety felt by children, we conducted a within-subjects study where children aged 8 to 11 years (n = 52) read aloud to a human and a robot individually while being monitored for physiological responses associated with anxiety. We found that children exhibited fewer physiological indicators of anxiety, specifically vocal jitter and heart rate variability, when reading to the robot compared with reading to a person. This paper provides strong evidence that a robot’s presence has an effect on the anxiety a person experiences while doing a task, offering justification for the use of robots in a wide-reaching array of social interactions that may be anxiety inducing.

流利的阅读是培养读写能力的重要组成部分，然而，练习流利的最好方法——大声朗读——可能会导致严重的焦虑，从而抑制读写能力的发展，对学生成年后产生不利影响。缓解口语阅读焦虑的一种很有希望的干预方法是让孩子对着机器人大声朗读。尽管先前的研究表明，人们在机器人面前比在人类面前更容易感到舒适，但很少有研究表明，与在人类面前相比，人们在机器人面前表现得更少焦虑，或者使用客观的生理指标来识别焦虑的减少。为了研究机器人阅读同伴是否可以减少儿童的阅读焦虑，我们进行了一项研究，让8至11岁的儿童（n = 52）分别对人类和机器人大声朗读，同时监测与焦虑相关的生理反应。我们发现，与给人朗读相比，孩子们在给机器人朗读时表现出更少的焦虑生理指标，尤其是声音抖动和心率变异。这篇论文提供了强有力的证据，证明机器人的存在对人们在完成任务时所经历的焦虑有影响，为在可能引起焦虑的广泛社会互动中使用机器人提供了理由。

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引用次数: 0

How can educational robots enhance family life? Through careful integration 教育机器人如何改善家庭生活？通过精心整合

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-09-10 DOI: 10.1126/scirobotics.adu6123

Joseph E. Michaelis, Bilge Mutlu

Family-centered integration is critical for the success of in-home educational robots.

以家庭为中心的整合对于家庭教育机器人的成功至关重要。

引用次数: 0

Observing a robot peer’s failures facilitates students’ classroom learning 观察机器人同伴的失败有助于学生的课堂学习

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-09-10 DOI: 10.1126/scirobotics.adu5257

Liuqing Chen, Yu Cai, Yuyang Fang, Ziqi Yang, Duowei Xia, Jiaxiang You, Shuhong Xiao, Yaxuan Song, Lingwei Zhan, Juanjuan Chen, Lingyun Sun

According to productive failure (PF) theory, experiencing failure during problem-solving can enhance students’ knowledge acquisition in subsequent instruction. However, challenging students with problems beyond their current capabilities may strain their skills, prior knowledge, and emotional well-being. To address this, we designed a social robot–assisted teaching activity in which students observed a robot’s unsuccessful problem-solving attempts, offering a PF-like preparatory effect without requiring direct failure. We conducted two classroom-based studies in a middle school setting to evaluate the method’s effectiveness. In study 1 (N = 135), we compared three instructional methods—observing robot failure (RF), individual problem-solving failure, and direct instruction—in an eighth-grade mathematics lesson. Students in the RF condition showed the greatest gains in conceptual understanding and reported lower social pressure, although no significant differences were found in procedural knowledge or knowledge transfer. Follow-up study 2 (N = 110) further validated the method’s effectiveness in supporting knowledge acquisition after a 2-week robot-involved adaptation phase, when the novelty effect had largely subsided. Students confirmed their perception of the robot as a peer, and they offered positive evaluations of its intelligence and neutral views of its anthropomorphism. Our findings suggest that observing the robot’s failure has a comparable, or even greater, effect on knowledge acquisition than experiencing failure firsthand. These results underscore the value of social robots as peers in science, technology, engineering, and mathematics education and highlight the potential of integrating robotics with evidence-based teaching strategies to enhance learning outcomes.

根据生产失败理论，在解决问题的过程中经历失败，可以促进学生在后续教学中的知识获取。然而，用超出他们现有能力的问题挑战学生可能会使他们的技能、先前的知识和情感健康受到影响。为了解决这个问题，我们设计了一个社交机器人辅助教学活动，在这个活动中，学生观察机器人不成功的解决问题的尝试，提供类似pf的准备效果，而不需要直接失败。我们在一所中学进行了两项以课堂为基础的研究，以评估该方法的有效性。在研究1 （N = 135）中，我们比较了八年级数学课上的三种教学方法——观察机器人失败（RF）、个人解决问题失败和直接指导。尽管在程序知识和知识转移方面没有发现显著差异，但RF条件下的学生在概念理解方面取得了最大的进步，并报告了较低的社会压力。后续研究2 （N = 110）进一步验证了该方法在2周机器人参与的适应阶段后支持知识获取的有效性，此时新颖性效应已基本消退。学生们确认了他们将机器人视为同伴的看法，他们对机器人的智能给出了积极的评价，对机器人的拟人化持中立态度。我们的研究结果表明，观察机器人的故障与亲身经历故障相比，对知识获取的影响相当，甚至更大。这些结果强调了社交机器人在科学、技术、工程和数学教育中的价值，并强调了将机器人技术与循证教学策略相结合以提高学习效果的潜力。

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引用次数: 0

Optogenetic neuromuscular actuation of a miniature electronic biohybrid robot 微型电子生物混合机器人的光遗传神经肌肉驱动

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-09-03 DOI: 10.1126/scirobotics.adu5830

Hyegi Min, Yue Wang, Jiaojiao Wang, Xiuyuan Li, Woong Kim, Onur Aydin, Sehong Kang, Jae-Sung You, Jongwon Lim, Katy Wolhaupter, Yikang Xu, Zhengguang Zhu, Jianyu Gu, Xinming Li, Yongdeok Kim, Tarun Rao, Hyun Joon Kong, Taher A. Saif, Yonggang Huang, John A. Rogers, Rashid Bashir

Neuronal control of skeletal muscle function is ubiquitous across species for locomotion and doing work. In particular, emergent behaviors of neurons in biohybrid neuromuscular systems can advance bioinspired locomotion research. Although recent studies have demonstrated that chemical or optogenetic stimulation of neurons can control muscular actuation through the neuromuscular junction (NMJ), the correlation between neuronal activities and resulting modulation in the muscle responses is less understood, hindering the engineering of high-level functional biohybrid systems. Here, we developed NMJ-based biohybrid crawling robots with optogenetic mouse motor neurons, skeletal muscles, 3D-printed hydrogel scaffolds, and integrated onboard wireless micro–light-emitting diode (μLED)–based optoelectronics. We investigated the coupling of the light stimulation and neuromuscular actuation through power spectral density (PSD) analysis. We verified the modulation of the mechanical functionality of the robot depending on the frequency of the optical stimulation to the neural tissue. We demonstrated continued muscle contraction up to 20 minutes after a 1-minute-long pulsed 2-hertz optical stimulation of the neural tissue. Furthermore, the robots were shown to maintain their mechanical functionality for more than 2 weeks. This study provides insights into reliable neuronal control with optoelectronics, supporting advancements in neuronal modulation, biohybrid intelligence, and automation.

神经控制骨骼肌的功能是无处不在的跨物种运动和做工作。特别是，生物混合神经肌肉系统中神经元的涌现行为可以促进生物激励运动的研究。尽管最近的研究表明，神经元的化学或光遗传刺激可以通过神经肌肉连接（NMJ）控制肌肉驱动，但神经元活动与肌肉反应调节之间的相关性尚不清楚，这阻碍了高水平功能生物杂交系统的工程设计。在这里，我们开发了基于nmj的生物混合爬行机器人，该机器人具有光遗传小鼠运动神经元、骨骼肌、3d打印水凝胶支架，以及集成了基于无线微发光二极管（μLED）的光电器件。通过功率谱密度（PSD）分析研究了光刺激与神经肌肉驱动的耦合关系。我们验证了机器人机械功能的调制取决于对神经组织的光刺激频率。我们证明了在对神经组织进行1分钟的脉冲2赫兹光学刺激后，肌肉持续收缩长达20分钟。此外，这些机器人的机械功能可以维持两周以上。这项研究为可靠的光电子神经元控制提供了见解，支持了神经元调制，生物混合智能和自动化的进步。

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引用次数: 0

RoboBallet: Planning for multirobot reaching with graph neural networks and reinforcement learning RoboBallet：基于图神经网络和强化学习的多机器人到达规划

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-09-03 DOI: 10.1126/scirobotics.ads1204

Matthew Lai, Keegan Go, Zhibin Li, Torsten Kröger, Stefan Schaal, Kelsey Allen, Jonathan Scholz

Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may be simple in isolation, automated joint task allocation, scheduling, and motion planning under spatiotemporal constraints remain computationally intractable for classical methods at real-world scales. Existing multiarm systems deployed in industry rely on human intuition and experience to design feasible trajectories manually in a labor-intensive process. To address this challenge, we propose a reinforcement learning (RL) framework to achieve automated task and motion planning, tested in an obstacle-rich environment with eight robots performing 40 reaching tasks in a shared workspace, where any robot can perform any task in any order. Our approach builds on a graph neural network (GNN) policy trained via RL on procedurally generated environments with diverse obstacle layouts, robot configurations, and task distributions. It uses a graph representation of scenes and a graph policy neural network trained through RL to generate trajectories of multiple robots, jointly solving the subproblems of task allocation, scheduling, and motion planning. Trained on large randomly generated task sets in simulation, our policy generalizes zero-shot to unseen settings with varying robot placements, obstacle geometries, and task poses. We further demonstrate that the high-speed capability of our solution enables its use in workcell layout optimization, improving solution times. The speed and scalability of our planner also open the door to capabilities such as fault-tolerant planning and online perception-based replanning, where rapid adaptation to dynamic task sets is required.

现代机器人制造要求多个机器人在共享的、充满障碍物的工作空间中无碰撞地协调完成众多任务。尽管单独的任务可能很简单，但在时空约束下的自动联合任务分配、调度和运动规划对于现实世界尺度的经典方法来说仍然难以计算。在工业中部署的现有多臂系统依靠人类的直觉和经验来手动设计可行的轨迹，这是一个劳动密集型的过程。为了应对这一挑战，我们提出了一个强化学习（RL）框架来实现自动化任务和运动规划，在一个充满障碍物的环境中进行测试，在一个共享工作空间中，8个机器人执行40个到达任务，其中任何机器人都可以以任何顺序执行任何任务。我们的方法建立在通过RL训练的图神经网络（GNN）策略上，该策略在程序生成的环境中具有不同的障碍布局、机器人配置和任务分布。它使用场景的图表示和经过强化学习训练的图策略神经网络生成多个机器人的轨迹，共同解决任务分配、调度和运动规划等子问题。在模拟中训练大型随机生成的任务集，我们的策略将零射击推广到具有不同机器人位置，障碍物几何形状和任务姿势的未见设置。我们进一步证明，我们的解决方案的高速功能使其能够用于工作单元布局优化，从而缩短解决时间。我们的规划器的速度和可伸缩性还为容错规划和基于在线感知的重新规划等功能打开了大门，这些功能需要对动态任务集进行快速适应。

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引用次数: 0

Good old-fashioned engineering can close the 100,000-year “data gap” in robotics 优秀的老式工程技术可以弥补机器人领域长达10万年的“数据缺口”

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-27 DOI: 10.1126/scirobotics.aea7390

Ken Goldberg

引用次数: 0

“Data will solve robotics and automation: True or false?”: A debate “数据将解决机器人和自动化问题：对还是错？”：一场辩论

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-27 DOI: 10.1126/scirobotics.aea7897

Nancy M. Amato, Seth Hutchinson, Animesh Garg, Aude Billard, Daniela Rus, Russ Tedrake, Frank Park, Ken Goldberg

Leading researchers debate the long-term influence of model-free methods that use large sets of demonstration data to train numerical generative models to control robots.

领先的研究人员争论无模型方法的长期影响，这种方法使用大量的演示数据来训练数值生成模型来控制机器人。

引用次数: 0

Explosion-powered eversible tactile displays 爆炸供电的隐形触觉显示器

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-27 DOI: 10.1126/scirobotics.adu2381

Ronald H. Heisser, Khoi D. Ly, Ofek Peretz, Young S. Kim, Carlos A. Diaz-Ruiz, Rachel M. Miller, Cameron A. Aubin, Sadaf Sobhani, Nikolaos Bouklas, Robert F. Shepherd

High-resolution electronic tactile displays stand to transform haptics for remote machine operation, virtual reality, and digital information access for people who are blind or visually impaired. Yet, increasing the resolution of these displays requires increasing the number of individually addressable actuators while simultaneously reducing their total surface area, power consumption, and weight, challenges most evidently reflected in the dearth of affordable multiline braille displays. Blending principles from soft robotics, microfluidics, and nonlinear mechanics, we introduce a 10-dot–by–10-dot array of 2-millimeter-diameter, combustion-powered, eversible soft actuators that individually rise in 0.24 milliseconds to repeatably produce display patterns. Our rubber architecture is hermetically sealed and demonstrates resistance to liquid and dirt ingress. We demonstrate complete actuation cycles in an untethered tactile display prototype. Our platform technology extends the capabilities of tactile displays to environments that are inaccessible to traditional actuation modalities.

高分辨率电子触觉显示器将改变触觉，为盲人或视障人士提供远程机器操作、虚拟现实和数字信息访问。然而，提高这些显示器的分辨率需要增加单个可寻址驱动器的数量，同时减少它们的总表面积、功耗和重量，这些挑战最明显地反映在价格合理的多行盲文显示器的缺乏上。融合了软机器人、微流体和非线性力学的原理，我们引入了一个直径为2毫米的10点乘10点阵列，燃烧驱动，可逆的软致动器，每个致动器在0.24毫秒内上升，以重复产生显示图案。我们的橡胶结构是密封的，可以抵抗液体和污垢的进入。我们在一个无系绳的触觉显示原型中演示了完整的驱动周期。我们的平台技术将触觉显示器的功能扩展到传统驱动模式无法进入的环境。

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引用次数: 0

Attention-based map encoding for learning generalized legged locomotion 基于注意的映射编码学习广义腿部运动

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-27 DOI: 10.1126/scirobotics.adv3604

Junzhe He, Chong Zhang, Fabian Jenelten, Ruben Grandia, Moritz Bächer, Marco Hutter

Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. Although traditional model-based controllers excel at planning on complex terrains, they struggle with real-world uncertainties. Learning-based controllers offer robustness to such uncertainties but often lack precision on terrains with sparse steppable areas. Hybrid methods achieve enhanced robustness on sparse terrains by combining both methods but are computationally demanding and constrained by the inherent limitations of model-based planners. To achieve generalized legged locomotion on diverse terrains while preserving the robustness of learning-based controllers, this paper proposes an attention-based map encoding conditioned on robot proprioception, which is trained as part of the controller using reinforcement learning. We show that the network learns to focus on steppable areas for future footholds when the robot dynamically navigates diverse and challenging terrains. We synthesized behaviors that exhibited robustness against uncertainties while enabling precise and agile traversal of sparse terrains. In addition, our method offers a way to interpret the topographical perception of a neural network. We have trained two controllers for a 12-degrees-of-freedom quadrupedal robot and a 23-degrees-of-freedom humanoid robot and tested the resulting controllers in the real world under various challenging indoor and outdoor scenarios, including ones unseen during training.

腿式机器人的动态运动是扩展移动机器人操作范围的一个关键而又具有挑战性的课题。当可能的立足点稀疏时，它需要精确的规划，对不确定性和干扰的鲁棒性，以及跨不同地形的通用性。尽管传统的基于模型的控制器擅长在复杂地形上进行规划，但它们难以应对现实世界的不确定性。基于学习的控制器对这种不确定性具有鲁棒性，但在具有稀疏可步进区域的地形上往往缺乏精度。混合方法通过结合两种方法来增强稀疏地形上的鲁棒性，但计算量大，并且受到基于模型的规划器固有局限性的约束。为了在保持基于学习的控制器的鲁棒性的同时实现在不同地形上的广义腿部运动，本文提出了一种基于机器人本体感觉的基于注意力的地图编码，并使用强化学习将其作为控制器的一部分进行训练。我们表明，当机器人动态导航各种具有挑战性的地形时，网络学会关注可行走的区域，为未来的立足点做好准备。我们合成了对不确定性表现出鲁棒性的行为，同时使稀疏地形的精确和敏捷遍历成为可能。此外，我们的方法提供了一种解释神经网络的地形感知的方法。我们已经为一个12自由度的四足机器人和一个23自由度的人形机器人训练了两个控制器，并在现实世界中测试了各种具有挑战性的室内和室外场景，包括训练中看不到的场景。

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引用次数: 0

Embodied intelligence paradigm for human-robot communication 人机交流的具身智能范式

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics

Pub Date : 2025-08-20 DOI: 10.1126/scirobotics.ads8528

Nana Obayashi, Arsen Abdulali, Fumiya Iida, Josie Hughes

Animals leverage their full embodiment to achieve multimodal, redundant, and subtle communication. To achieve the same for robots, they must similarly exploit their brain-body-environment interactions or their embodied intelligence. To advance this approach, we propose a framework building on Shannon’s information channel theory for communication to provide the key principles and benchmarks for advancing human-robot communication.

动物充分利用它们的化身来实现多模式、冗余和微妙的交流。为了让机器人实现同样的目标，它们必须同样利用它们的大脑-身体-环境相互作用或它们的具身智能。为了推进这种方法，我们提出了一个基于Shannon通信信息通道理论的框架，为推进人机通信提供关键原则和基准。

引用次数: 0

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