Science Robotics最新文献

NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation 神经感觉与神经场手部操作的视觉触觉感知

IF 26.1 1区计算机科学

Science Robotics Pub Date : 2024-11-13 DOI: 10.1126/scirobotics.adl0628

Sudharshan Suresh, Haozhi Qi, Tingfan Wu, Taosha Fan, Luis Pineda, Mike Lambeta, Jitendra Malik, Mrinal Kalakrishnan, Roberto Calandra, Michael Kaess, Joseph Ortiz, Mustafa Mukadam

{"title":"NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation","authors":"Sudharshan Suresh, Haozhi Qi, Tingfan Wu, Taosha Fan, Luis Pineda, Mike Lambeta, Jitendra Malik, Mrinal Kalakrishnan, Roberto Calandra, Michael Kaess, Joseph Ortiz, Mustafa Mukadam","doi":"10.1126/scirobotics.adl0628","DOIUrl":"10.1126/scirobotics.adl0628","url":null,"abstract":"<div >To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, such spatial awareness involves estimating the object’s pose and shape. The status quo for in-hand perception primarily uses vision and is restricted to tracking a priori known objects. Moreover, visual occlusion of objects in hand is imminent during manipulation, preventing current systems from pushing beyond tasks without occlusion. We combined vision and touch sensing on a multifingered hand to estimate an object’s pose and shape during in-hand manipulation. Our method, NeuralFeels, encodes object geometry by learning a neural field online and jointly tracks it by optimizing a pose graph problem. We studied multimodal in-hand perception in simulation and the real world, interacting with different objects via a proprioception-driven policy. Our experiments showed final reconstruction <i>F</i> scores of 81% and average pose drifts of 4.7 millimeters, which was further reduced to 2.3 millimeters with known object models. In addition, we observed that, under heavy visual occlusion, we could achieve improvements in tracking up to 94% compared with vision-only methods. Our results demonstrate that touch, at the very least, refines and, at the very best, disambiguates visual estimates during in-hand manipulation. We release our evaluation dataset of 70 experiments, FeelSight, as a step toward benchmarking in this domain. Our neural representation driven by multimodal sensing can serve as a perception backbone toward advancing robot dexterity.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"9 96","pages":""},"PeriodicalIF":26.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/scirobotics.adl0628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142601978","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

Crucial hurdles to achieving human-robot harmony 实现人与机器人和谐相处的关键障碍。

IF 26.1 1区计算机科学

Science Robotics Pub Date : 2024-11-13 DOI: 10.1126/scirobotics.adp2507

Keya Ghonasgi, Taylor Higgins, Meghan E. Huber, Marcia K. O’Malley

引用次数: 0

How much initiative should a service robot have? 服务机器人应该有多少主动权？

IF 26.1 1区计算机科学

Science Robotics Pub Date : 2024-11-13 DOI: 10.1126/scirobotics.adt8902

Robin R. Murphy

引用次数: 0

Self-organizing nervous systems for robot swarms 机器人群的自组织神经系统

IF 26.1 1区计算机科学

Science Robotics Pub Date : 2024-11-13 DOI: 10.1126/scirobotics.adl5161

Weixu Zhu, Sinan Oğuz, Mary Katherine Heinrich, Michael Allwright, Mostafa Wahby, Anders Lyhne Christensen, Emanuele Garone, Marco Dorigo

{"title":"Self-organizing nervous systems for robot swarms","authors":"Weixu Zhu, Sinan Oğuz, Mary Katherine Heinrich, Michael Allwright, Mostafa Wahby, Anders Lyhne Christensen, Emanuele Garone, Marco Dorigo","doi":"10.1126/scirobotics.adl5161","DOIUrl":"10.1126/scirobotics.adl5161","url":null,"abstract":"<div >We present the self-organizing nervous system (SoNS), a robot swarm architecture based on self-organized hierarchy. The SoNS approach enables robots to autonomously establish, maintain, and reconfigure dynamic multilevel system architectures. For example, a robot swarm consisting of <i>n</i> independent robots could transform into a single <i>n</i>–robot SoNS and then into several independent smaller SoNSs, where each SoNS uses a temporary and dynamic hierarchy. Leveraging the SoNS approach, we showed that sensing, actuation, and decision-making can be coordinated in a locally centralized way without sacrificing the benefits of scalability, flexibility, and fault tolerance, for which swarm robotics is usually studied. In several proof-of-concept robot missions—including binary decision-making and search and rescue—we demonstrated that the capabilities of the SoNS approach greatly advance the state of the art in swarm robotics. The missions were conducted with a real heterogeneous aerial-ground robot swarm, using a custom-developed quadrotor platform. We also demonstrated the scalability of the SoNS approach in swarms of up to 250 robots in a physics-based simulator and demonstrated several types of system fault tolerance in simulation and reality.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"9 96","pages":""},"PeriodicalIF":26.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/scirobotics.adl5161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142602028","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

Effect of hip abduction assistance on metabolic cost and balance during human walking 髋关节外展辅助对人体行走过程中代谢成本和平衡的影响。

IF 25 1区计算机科学

Science Robotics Pub Date : 2023-10-25 DOI: 10.1126/scirobotics.ade0876

Juneil Park, Kimoon Nam, Juseok Yun, JunYoung Moon, JaeWook Ryu, Sungjin Park, Seungtae Yang, Alireza Nasirzadeh, Woochul Nam, Sruthi Ramadurai, Myunghee Kim, Giuk Lee

{"title":"Effect of hip abduction assistance on metabolic cost and balance during human walking","authors":"Juneil Park, Kimoon Nam, Juseok Yun, JunYoung Moon, JaeWook Ryu, Sungjin Park, Seungtae Yang, Alireza Nasirzadeh, Woochul Nam, Sruthi Ramadurai, Myunghee Kim, Giuk Lee","doi":"10.1126/scirobotics.ade0876","DOIUrl":"10.1126/scirobotics.ade0876","url":null,"abstract":"<div >The use of wearable robots to provide walking assistance has rapidly grown over the past decade, with notable advances made in robot design and control methods toward reducing physical effort while performing an activity. The reduction in walking effort has mainly been achieved by assisting forward progression in the sagittal plane. Human gait, however, is a complex movement that combines motions in three planes, not only the sagittal but also the transverse and frontal planes. In the frontal plane, the hip joint plays a key role in gait, including balance. However, wearable robots targeting this motion have rarely been investigated. In this study, we developed a hip abduction assistance wearable robot by formulating the hypothesis that assistance that mimics the biological hip abduction moment or power could reduce the metabolic cost of walking and affect the dynamic balance. We found that hip abduction assistance with a biological moment second peak mimic profile reduced the metabolic cost of walking by 11.6% compared with the normal walking condition. The assistance also influenced balance-related parameters, including the margin of stability. Hip abduction assistance influenced the center-of-mass movement in the mediolateral direction. When the robot assistance was applied as the center of mass moved toward the opposite leg, the assistance replaced some of the efforts that would have otherwise been provided by the human. This indicates that hip abduction assistance can reduce physical effort during human walking while influencing balance.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"8 83","pages":""},"PeriodicalIF":25.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50163885","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

Touching reality: Bridging the user-researcher divide in upper-limb prosthetics 感人的现实：弥合用户和研究人员在上肢假肢方面的分歧。

IF 25 1区计算机科学

Science Robotics Pub Date : 2023-10-25 DOI: 10.1126/scirobotics.adk9421

J. D. Brown, E. Battaglia, S. Engdahl, G. Levay, A. C. Parks, E. Skinner, M. K. O’Malley

引用次数: 0

A powered prosthesis supports weight-bearing stand-to-sit transitions 一个动力假肢支持承重的站-坐转换。

IF 25 1区计算机科学

Science Robotics Pub Date : 2023-10-25 DOI: 10.1126/scirobotics.adl3007

Amos Matsiko

引用次数: 0

The role of collaborative robotics in assistive and rehabilitation applications 协作机器人在辅助和康复应用中的作用。

IF 25 1区计算机科学

Science Robotics Pub Date : 2023-10-25 DOI: 10.1126/scirobotics.adk6743

Monroe Kennedy III

引用次数: 0

Assistive robotics should seamlessly integrate humans and robots 辅助机器人应该将人类和机器人无缝集成。

IF 25 1区计算机科学

Science Robotics Pub Date : 2023-10-25 DOI: 10.1126/scirobotics.adl0014

Douglas Weber, Amos Matsiko

引用次数: 0

User preference optimization for control of ankle exoskeletons using sample efficient active learning 使用样本有效主动学习的脚踝外骨骼控制的用户偏好优化。

IF 25 1区计算机科学

Science Robotics Pub Date : 2023-10-18 DOI: 10.1126/scirobotics.adg3705

Ung Hee Lee, Varun S. Shetty, Patrick W. Franks, Jie Tan, Georgios Evangelopoulos, Sehoon Ha, Elliott J. Rouse

{"title":"User preference optimization for control of ankle exoskeletons using sample efficient active learning","authors":"Ung Hee Lee, Varun S. Shetty, Patrick W. Franks, Jie Tan, Georgios Evangelopoulos, Sehoon Ha, Elliott J. Rouse","doi":"10.1126/scirobotics.adg3705","DOIUrl":"10.1126/scirobotics.adg3705","url":null,"abstract":"<div >One challenge to achieving widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are “tuned” to optimize a physiological or biomechanical objective. However, these approaches are resource intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is likely derived from many factors, including comfort, fatigue, and stability, among others. This work introduces an approach to conveniently tune the four parameters of an exoskeleton controller to maximize user preference. Our overarching strategy is to leverage the wearer to internally balance the experiential factors of wearing the system. We used an evolutionary algorithm to recommend potential parameters, which were ranked by a neural network that was pretrained with previously collected user preference data. The controller parameters that had the highest preference ranking were provided to the exoskeleton, and the wearer responded with real-time feedback as a forced-choice comparison. Our approach was able to converge on controller parameters preferred by the wearer with an accuracy of 88% on average when compared with randomly generated parameters. User-preferred settings stabilized in 43 ± 7 queries. This work demonstrates that user preference can be leveraged to tune a partial-assist ankle exoskeleton in real time using a simple, intuitive interface, highlighting the potential for translating lower-limb wearable technologies into our daily lives.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"8 83","pages":""},"PeriodicalIF":25.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685403","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