Science Robotics最新文献

{"title":"Robot in a crib: How a playing robot helps us understand sensorimotor contingency learning","authors":"Josua Spisak, Sergiu Tcaci Popescu, Lukas Rustler, Stefan Wermter, J. Kevin O’Regan, Matej Hoffmann","doi":"10.1126/scirobotics.aed4106","DOIUrl":"https://doi.org/10.1126/scirobotics.aed4106","url":null,"abstract":"Learning sensorimotor contingencies—that is, the link between one’s actions and their sensory effects—is fundamental to developing body knowledge, understanding causality, and developing a sense of agency. In developmental psychology, this process is classically studied using the mobile paradigm, where infants learn that movement of a limb causes motion of a connected mobile. To expand our understanding of how infants learn this, we tested an embodied computational model that learns through two biologically inspired mechanisms: prediction and curiosity. Implemented on the child-sized iCub humanoid robot interacting with a mobile, the model detected sensorimotor contingencies across several experimental conditions using a variety of movement strategies. Our findings suggest that contingency learning cannot be captured by a single behavioral metric, such as the amount of movement, but instead emerges through a spectrum of exploratory behaviors. Analysis of the robot’s internal activity reveals that these behaviors emerge from the dynamic trade-off between prediction and curiosity—between exploitation and exploration. Our work provides a biologically motivated, physically embodied model of sensorimotor interaction that connects theories of infant learning with robotic implementations. The results allow us to generate testable hypotheses for developmental research and to inform the design of autonomous learning systems.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"35 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815569","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":"Convergent binocular stereo: Depth perception for humanoid robot vision","authors":"Mingshi Chi, John K. Tsotsos","doi":"10.1126/scirobotics.aec7205","DOIUrl":"https://doi.org/10.1126/scirobotics.aec7205","url":null,"abstract":"The design of robotic binocular camera systems has been inspired by human vision, as has their use in humanoid robots. One aspect of this inspiration has yet to play a major role, namely, that humans determine depth using a convergent binocular imaging geometry with both eyes pointing at the same location. Although some robot heads have the functionality to use vergence and version movements and thus alter binocular imaging geometry, a method to exploit it for depth computation has not been explored. Even though to an observer, their motions and appearance might resemble that of humans, in detail, the resemblance to the motions required for actual convergent depth computation is not seen. To bridge this gap, we present convergent binocular stereo (CBS), a stereo algorithm designed to provide a foundation for purposeful binocular computations under the humanoid constraint, intended for active binocular robots. CBS computes horizontal and vertical disparities using a coarse-to-fine refinement strategy with Gabor-filtered responses. We also introduce the Convergent Binocular Stereo–BenchMark (CBS-BM), a convergent, natural image dataset containing 49 scenes with ground-truth horizontal disparity collected on a four-degrees-of-freedom robotic system. Our evaluation, a quantitative comparison between parallel and convergent stereo systems, shows that CBS is broadly competitive with state-of-the-art parallel methods, even outperforming them in scenes with repeated patterns and in mean horizontal disparity and depth error over all scenes. Although not intended to replace parallel stereo where humanlike behavior is unnecessary, CBS enables functionally realistic depth computation for humanoid robotic heads.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"126 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815568","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":"From acrobatics to generality: Humanoid robots at an inflection point.","authors":"Hae-Won Park","doi":"10.1126/scirobotics.aek7970","DOIUrl":"https://doi.org/10.1126/scirobotics.aek7970","url":null,"abstract":"<p><p>This special issue examines what it takes to move humanoids beyond task-specific demonstrations toward dependable generality.</p>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"11 117","pages":"eaek7970"},"PeriodicalIF":25.5,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148834898","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":"BeyondMimic: From motion tracking to versatile humanoid control via guided diffusion","authors":"Qiayuan Liao, Takara E. Truong, Xiaoyu Huang, Yuman Gao, Guy Tevet, Koushil Sreenath, C. Karen Liu","doi":"10.1126/scirobotics.adx8924","DOIUrl":"https://doi.org/10.1126/scirobotics.adx8924","url":null,"abstract":"The humanlike form of humanoid robots uniquely positions them to achieve the agility and versatility in motor skills that humans have. Learning from human demonstrations offers a scalable approach to acquiring these capabilities. However, prior works either produced unnatural motions or relied on motion-specific tuning to achieve satisfactory naturalness. Furthermore, these methods are often motion or goal specific, lacking the versatility to compose diverse skills, especially when solving unseen tasks. We present BeyondMimic, a framework that scales to diverse motions and carries the versatility to compose them seamlessly in tackling unseen downstream tasks. A compact motion tracking formulation enables mastery of a wide range of highly agile behaviors, including aerial cartwheels, spin kicks, flip kicks, and sprinting, with a single setup and shared hyperparameters, all while achieving humanlike performance. Moving beyond the mere imitation of existing motions, we propose a unified latent diffusion model that empowers versatile goal specification, seamless task switching, and dynamic composition of these agile behaviors. Leveraging classifier guidance, a diffusion-specific technique for test-time optimization toward unseen objectives, our model extended its capability to solve downstream tasks never encountered during training, including motion inpainting, joystick teleoperation, and obstacle avoidance, and transferred these skills zero-shot to real hardware. Together, these components enable scalable acquisition of humanlike motor skills from human motion and motion synthesis that generalizes and adapts beyond the training setup.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"31 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815571","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":"Learning vision-driven reactive soccer skills for humanoid robots","authors":"Yushi Wang, Changsheng Luo, Penghui Chen, Jianran Liu, Weijian Sun, Tong Guo, Kechang Yang, Biao Hu, Yangang Zhang, Mingguo Zhao","doi":"10.1126/scirobotics.aed1152","DOIUrl":"https://doi.org/10.1126/scirobotics.aed1152","url":null,"abstract":"Humanoid soccer poses a representative challenge for embodied intelligence, requiring robots to coordinate agile locomotion with unreliable visual perception in dynamic environments. However, existing systems typically rely on modular pipelines that separate perception from control or assume ideal sensing, making it difficult to achieve coherent and reactive behavior under real-world perceptual limitations. In this work, we present a unified reinforcement learning–based controller that enables humanoid robots to learn vision-driven reactive soccer skills by directly coupling visual perception with locomotion control. The robot is trained in simulation to acquire soccer behaviors, and adversarial motion priors guide policy learning toward natural motion patterns. To support robust performance under imperfect sensing, we introduce an encoder-decoder architecture together with a virtual perception system that models key characteristics of onboard vision, exposing the policy to perceptual noise and detection failures during training. This design encourages the policy to internalize perceptual uncertainty and continuously adapt its motion in a closed loop. The resulting controller produces coordinated soccer behaviors using only onboard vision, including ball searching, chasing, and multidirectional kicking. It reduces ball position estimation error by 46% and shortens time-to-kick by up to 64% compared with a rule-based baseline, achieving around 90% kicking success in frontfield positions. Experiments across diverse environments and dynamic scenarios, including real RoboCup competitions, further demonstrate the robust performance of the controller. These results highlight the practical effectiveness of integrating perceptual uncertainty directly into policy learning for achieving reliable vision-driven behaviors in humanoid robots operating under real-world conditions.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"4 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766796","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":"Are humanoid robots in 2035 more fiction than science?","authors":"Robin R. Murphy","doi":"10.1126/scirobotics.aek6923","DOIUrl":"https://doi.org/10.1126/scirobotics.aek6923","url":null,"abstract":"The Will Smith 2004 blockbuster <jats:italic toggle=\"yes\">I, Robot</jats:italic> predicts pervasive humanoid robots in 2035; investors agree, roboticists disagree.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"100 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766798","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":"Evolution of humanoid locomotion control","authors":"Yan Gu, Guanya Shi, Fan Shi, I-Chia Chang, Yen-Jen Wang, Qilong Cheng, Zachary Olkin, Ivan Lopez-Sanchez, Yunchu Feng, Jian Zhang, Aaron D. Ames, Hao Su, Koushil Sreenath","doi":"10.1126/scirobotics.aed3973","DOIUrl":"https://doi.org/10.1126/scirobotics.aed3973","url":null,"abstract":"Humanoid robots stand at the forefront of robotics, aiming to capture the agility, robustness, and expressivity of human movement in an anthropomorphic form. The locomotion control of humanoids has evolved from classical model–based methods to reinforcement learning powered by large-scale simulation and now to generative models that produce adaptive, whole-body behaviors, propelling humanoids toward operation in real-world environments. This survey positions humanoid control at a turning point, converging toward a unified paradigm of physics-guided generative intelligence that integrates optimization, learning, and predictive reasoning. We identify three core principles linking these paradigms: physics-based modeling, constrained decision-making, and adaptation to uncertainty. Building on these connections, we provide recommendations for researchers and outline open challenges in safety, accessibility, and human-level capability. These directions represent a transformation from engineered stability to intelligent autonomy, laying the groundwork for humanoid generalists capable of operating safely, collaborating naturally, and extending human capability in the open world.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"8 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766797","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":"SONIC: Supersizing motion tracking for natural humanoid whole-body control","authors":"Zhengyi Luo, Ye Yuan, Tingwu Wang, Chenran Li, Fernando Castañeda, Sirui Chen, Zi-Ang Cao, Jiefeng Li, David Minor, Qingwei Ben, Jinhyung Park, David Sami, Zi Wang, Xingye Da, Runyu Ding, Cyrus Hogg, Lina Song, Edy Lim, Eugene Jeong, Tairan He, Haoru Xue, Wenli Xiao, Simon Yuen, Jan Kautz, Yan Chang, Umar Iqbal, Linxi “Jim” Fan, Yuke Zhu","doi":"10.1126/scirobotics.aed4592","DOIUrl":"https://doi.org/10.1126/scirobotics.aed4592","url":null,"abstract":"Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for humanoid control. Current neural controllers for humanoids remain modest in size, target a limited set of behaviors, and are trained on a handful of GPUs. We show that scaling model capacity, data, and compute yields a generalist humanoid controller capable of natural, robust whole-body movements. We position motion tracking as a scalable task for humanoid control, leveraging dense supervision from diverse motion-capture data to acquire human motion priors without manual reward engineering. We build a foundation model for motion tracking by scaling along three axes: network size (1.2 to 42 million parameters), dataset volume (more than 100 million frames from 700 hours of motion capture), and compute (21,000 GPU hours). Beyond demonstrating the benefits of scale, we further show downstream utility through a real-time kinematic planner that bridges motion tracking to tasks such as navigation, enabling natural and interactive control, as well as a unified token space that supports virtual reality (VR) teleoperation and vision-language-action (VLA) models with a single policy. Through this interface, we demonstrate autonomous VLA-driven whole-body locomanipulation requiring coordinated hand and foot placement. Scaling motion tracking exhibits favorable properties: Performance improves steadily with compute and data diversity, and learned policies generalize to unseen motions, establishing motion tracking at scale as a practical foundation for humanoid control.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"278 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148709536","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":"Physical AI is enabled by mechanical hardware.","authors":"Jonathan Hurst","doi":"10.1126/scirobotics.aee2921","DOIUrl":"10.1126/scirobotics.aee2921","url":null,"abstract":"<p><p>We are entering an era where AI enables multipurpose applications, but the mechanical hardware must be done right.</p>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"11 117","pages":"eaee2921"},"PeriodicalIF":25.5,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148724383","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":"ZEST: Zero-shot embodied skill transfer for athletic robot control","authors":"Jean Pierre Sleiman, He Li, Alphonsus Adu-Bredu, Robin Deits, Arun Kumar, Kevin Bergamin, Mohak Bhardwaj, Scott Biddlestone, Nicola Burger, Matthew A. Estrada, Francesco Iacobelli, Twan Koolen, Alexander Lambert, Erica Lin, M. Eva Mungai, Zach Nobles, Shane Rozen-Levy, Yuyao Shi, Jiashun Wang, Jakob Welner, Fangzhou Yu, Mike Zhang, Alfred Rizzi, Jessica Hodgins, Sylvain Bertrand, Yeuhi Abe, Scott Kuindersma, Farbod Farshidian","doi":"10.1126/scirobotics.aec7695","DOIUrl":"https://doi.org/10.1126/scirobotics.aec7695","url":null,"abstract":"Achieving robust, humanlike whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittle process of tuning controllers. We introduce ZEST (zero-shot embodied skill transfer), a streamlined motion-imitation framework that trains policies via reinforcement learning from diverse sources—high-fidelity motion capture, noisy monocular video, and non–physics-constrained animation—and deploys them to hardware zero-shot. ZEST generalizes across behaviors and platforms without relying on contact labels, reference or observation windows, state estimators, or extensive reward shaping. Its training pipeline combines adaptive sampling, which focuses training on difficult motion segments, and an automatic curriculum using a model-based assistive wrench, together enabling dynamic, long-horizon maneuvers. We further provide a procedure for selecting joint-level gains from approximate analytical armature values for closed-chain actuators, along with a refined model of actuators. Trained entirely in simulation with moderate domain randomization, ZEST demonstrated broad generality. On Boston Dynamics’ Atlas humanoid, ZEST learned dynamic, multicontact skills (army crawl and breakdancing) from motion capture. It transferred expressive dance and scene-interaction skills, such as box climbing, directly from videos to Atlas and the Unitree G1. Furthermore, it extended across morphologies to the Spot quadruped, enabling acrobatics, such as a continuous backflip, through animation. Together, these results demonstrate robust zero-shot deployment across heterogeneous data sources and embodiments, establishing ZEST as a scalable interface between biological movements and their robotic counterparts.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"3 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148709535","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}