Pub Date : 2025-02-12DOI: 10.1126/scirobotics.adr6472
John F. Zimmerman, Daniel J. Drennan, James Ikeda, Qianru Jin, Herdeline Ann M. Ardoña, Sean L. Kim, Ryoma Ishii, Kevin Kit Parker
In biomimetic design, researchers recreate existing biological structures to form functional devices. For biohybrid robotic swimmers assembled with tissue engineering, this is problematic because most devices operate at different length scales than their naturally occurring counterparts, resulting in reduced performance. To overcome these challenges, here, we demonstrate how machine learning–directed optimization (ML-DO) can be used to inform the design of a biohybrid robot, outperforming other nonlinear optimization techniques, such as Bayesian optimization, in the selection of high-performance geometries. We show how this approach can be used to maximize the thrust generated by a tissue-engineered mobuliform miniray. This results in devices that can swim at the millimeter scale while more closely preserving natural locomotive scaling laws. Overall, this work provides a quantitatively rigorous approach for the engineering design of muscular structure-function relationships in an automated fashion.
{"title":"Bioinspired design of a tissue-engineered ray with machine learning","authors":"John F. Zimmerman, Daniel J. Drennan, James Ikeda, Qianru Jin, Herdeline Ann M. Ardoña, Sean L. Kim, Ryoma Ishii, Kevin Kit Parker","doi":"10.1126/scirobotics.adr6472","DOIUrl":"https://doi.org/10.1126/scirobotics.adr6472","url":null,"abstract":"In biomimetic design, researchers recreate existing biological structures to form functional devices. For biohybrid robotic swimmers assembled with tissue engineering, this is problematic because most devices operate at different length scales than their naturally occurring counterparts, resulting in reduced performance. To overcome these challenges, here, we demonstrate how machine learning–directed optimization (ML-DO) can be used to inform the design of a biohybrid robot, outperforming other nonlinear optimization techniques, such as Bayesian optimization, in the selection of high-performance geometries. We show how this approach can be used to maximize the thrust generated by a tissue-engineered mobuliform miniray. This results in devices that can swim at the millimeter scale while more closely preserving natural locomotive scaling laws. Overall, this work provides a quantitatively rigorous approach for the engineering design of muscular structure-function relationships in an automated fashion.","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"65 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393478","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}
Pub Date : 2025-01-29DOI: 10.1126/scirobotics.ado7696
Woo-Young Choi, Woongbae Kim, Jae-Ryeong Choi, Sung Yol Yu, Seunguk Moon, Yong-Jai Park, Kyu-Jin Cho
Snap-through, a rapid transition of a system from an equilibrium state to a nonadjacent equilibrium state, is a valuable design element of soft devices for converting a monolithic stimulus into systematic responses with impulsive motions. A common way to benefit from snap-through is to embody it within structures and materials, such as bistable structures. Torque-reversal mechanisms discovered in nature, which harness snap-through instability via muscular forces, may have comparative advantages. However, the current intricacy of artificial torque-reversal mechanisms, which require sophisticated kinematics/kinetics, constrains design possibilities for soft joints and devices. Here, we harnessed hyperelasticity to implement a torque-reversal mechanism in a soft joint, generating repetitive cilia-like beating motions through an embedded tendon. The developed hyperelastic torque-reversal mechanism (HeTRM) exhibits transient bistability under a specific compressive displacement/force threshold, with snap-through occurring at the point where the transience ends. To validate the effectiveness of this design principle, we explored the functionalities of HeTRM in energy storage and release, dual modes for impulsive and continuous motion, mechanical fuse, and rapid three-dimensional motions, through proof-of-concept soft machines. We expect that this design principle provides insight into incorporating snap-through behavior in soft machines and may aid in understanding the relationship between torque-reversal mechanisms and bistability.
{"title":"A hyperelastic torque-reversal mechanism for soft joints with compression-responsive transient bistability","authors":"Woo-Young Choi, Woongbae Kim, Jae-Ryeong Choi, Sung Yol Yu, Seunguk Moon, Yong-Jai Park, Kyu-Jin Cho","doi":"10.1126/scirobotics.ado7696","DOIUrl":"10.1126/scirobotics.ado7696","url":null,"abstract":"<div >Snap-through, a rapid transition of a system from an equilibrium state to a nonadjacent equilibrium state, is a valuable design element of soft devices for converting a monolithic stimulus into systematic responses with impulsive motions. A common way to benefit from snap-through is to embody it within structures and materials, such as bistable structures. Torque-reversal mechanisms discovered in nature, which harness snap-through instability via muscular forces, may have comparative advantages. However, the current intricacy of artificial torque-reversal mechanisms, which require sophisticated kinematics/kinetics, constrains design possibilities for soft joints and devices. Here, we harnessed hyperelasticity to implement a torque-reversal mechanism in a soft joint, generating repetitive cilia-like beating motions through an embedded tendon. The developed hyperelastic torque-reversal mechanism (HeTRM) exhibits transient bistability under a specific compressive displacement/force threshold, with snap-through occurring at the point where the transience ends. To validate the effectiveness of this design principle, we explored the functionalities of HeTRM in energy storage and release, dual modes for impulsive and continuous motion, mechanical fuse, and rapid three-dimensional motions, through proof-of-concept soft machines. We expect that this design principle provides insight into incorporating snap-through behavior in soft machines and may aid in understanding the relationship between torque-reversal mechanisms and bistability.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 98","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056632","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}
Pub Date : 2025-01-29DOI: 10.1126/scirobotics.adp5005
Keya Ghonasgi, Kyle J. Kaveny, David Langlois, Leifur D. Sigurðarson, Tim A. Swift, Jason Wheeler, Aaron J. Young
Deploying machine vision for wearable robot control faces challenges in terms of usability, reliability, privacy, and costs.
{"title":"The case against machine vision for the control of wearable robotics: Challenges for commercial adoption","authors":"Keya Ghonasgi, Kyle J. Kaveny, David Langlois, Leifur D. Sigurðarson, Tim A. Swift, Jason Wheeler, Aaron J. Young","doi":"10.1126/scirobotics.adp5005","DOIUrl":"10.1126/scirobotics.adp5005","url":null,"abstract":"<div >Deploying machine vision for wearable robot control faces challenges in terms of usability, reliability, privacy, and costs.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 98","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069866","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}
Pub Date : 2025-01-29DOI: 10.1126/scirobotics.ado6187
Yunfan Ren, Fangcheng Zhu, Guozheng Lu, Yixi Cai, Longji Yin, Fanze Kong, Jiarong Lin, Nan Chen, Fu Zhang
Micro air vehicles (MAVs) capable of high-speed autonomous navigation in unknown environments have the potential to improve applications like search and rescue and disaster relief, where timely and safe navigation is critical. However, achieving autonomous, safe, and high-speed MAV navigation faces systematic challenges, necessitating reduced vehicle weight and size for high-speed maneuvering, strong sensing capability for detecting obstacles at a distance, and advanced planning and control algorithms maximizing flight speed while ensuring obstacle avoidance. Here, we present the safety-assured high-speed aerial robot (SUPER), a compact MAV with a 280-millimeter wheelbase and a thrust-to-weight ratio greater than 5.0, enabling agile flight in cluttered environments. SUPER uses a lightweight three-dimensional light detection and ranging (LIDAR) sensor for accurate, long-range obstacle detection. To ensure high-speed flight while maintaining safety, we introduced an efficient planning framework that directly plans trajectories using LIDAR point clouds. In each replanning cycle, two trajectories were generated: one in known free spaces to ensure safety and another in both known and unknown spaces to maximize speed. Compared with baseline methods, this framework reduced failure rates by 35.9 times while flying faster and with half the planning time. In real-world tests, SUPER achieved autonomous flights at speeds exceeding 20 meters per second, successfully avoiding thin obstacles and navigating narrow spaces. SUPER represents a milestone in autonomous MAV systems, bridging the gap from laboratory research to real-world applications.
{"title":"Safety-assured high-speed navigation for MAVs","authors":"Yunfan Ren, Fangcheng Zhu, Guozheng Lu, Yixi Cai, Longji Yin, Fanze Kong, Jiarong Lin, Nan Chen, Fu Zhang","doi":"10.1126/scirobotics.ado6187","DOIUrl":"10.1126/scirobotics.ado6187","url":null,"abstract":"<div >Micro air vehicles (MAVs) capable of high-speed autonomous navigation in unknown environments have the potential to improve applications like search and rescue and disaster relief, where timely and safe navigation is critical. However, achieving autonomous, safe, and high-speed MAV navigation faces systematic challenges, necessitating reduced vehicle weight and size for high-speed maneuvering, strong sensing capability for detecting obstacles at a distance, and advanced planning and control algorithms maximizing flight speed while ensuring obstacle avoidance. Here, we present the safety-assured high-speed aerial robot (SUPER), a compact MAV with a 280-millimeter wheelbase and a thrust-to-weight ratio greater than 5.0, enabling agile flight in cluttered environments. SUPER uses a lightweight three-dimensional light detection and ranging (LIDAR) sensor for accurate, long-range obstacle detection. To ensure high-speed flight while maintaining safety, we introduced an efficient planning framework that directly plans trajectories using LIDAR point clouds. In each replanning cycle, two trajectories were generated: one in known free spaces to ensure safety and another in both known and unknown spaces to maximize speed. Compared with baseline methods, this framework reduced failure rates by 35.9 times while flying faster and with half the planning time. In real-world tests, SUPER achieved autonomous flights at speeds exceeding 20 meters per second, successfully avoiding thin obstacles and navigating narrow spaces. SUPER represents a milestone in autonomous MAV systems, bridging the gap from laboratory research to real-world applications.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 98","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/scirobotics.ado6187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056756","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}
Pub Date : 2025-01-22DOI: 10.1126/scirobotics.adv4627
Amos Matsiko
Learning complex behaviors by humanoid robots could be achieved with natural interactions aided by large language models.
{"title":"Humanoid robot learning of complex behaviors with LLMs","authors":"Amos Matsiko","doi":"10.1126/scirobotics.adv4627","DOIUrl":"10.1126/scirobotics.adv4627","url":null,"abstract":"<div >Learning complex behaviors by humanoid robots could be achieved with natural interactions aided by large language models.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 98","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025797","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}
Pub Date : 2025-01-22DOI: 10.1126/scirobotics.adp0751
Prasanna Vijayaraghavan, Jeffrey Frederic Queißer, Sergio Verduzco Flores, Jun Tani
Humans excel at applying learned behavior to unlearned situations. A crucial component of this generalization behavior is our ability to compose/decompose a whole into reusable parts, an attribute known as compositionality. One of the fundamental questions in robotics concerns this characteristic: How can linguistic compositionality be developed concomitantly with sensorimotor skills through associative learning, particularly when individuals only learn partial linguistic compositions and their corresponding sensorimotor patterns? To address this question, we propose a brain-inspired neural network model that integrates vision, proprioception, and language into a framework of predictive coding and active inference on the basis of the free-energy principle. The effectiveness and capabilities of this model were assessed through various simulation experiments conducted with a robot arm. Our results show that generalization in learning to unlearned verb-noun compositions is significantly enhanced when training variations of task composition are increased. We attribute this to self-organized compositional structures in linguistic latent state space being influenced substantially by sensorimotor learning. Ablation studies show that visual attention and working memory are essential to accurately generate visuomotor sequences to achieve linguistically represented goals. These insights advance our understanding of mechanisms underlying development of compositionality through interactions of linguistic and sensorimotor experience.
{"title":"Development of compositionality through interactive learning of language and action of robots","authors":"Prasanna Vijayaraghavan, Jeffrey Frederic Queißer, Sergio Verduzco Flores, Jun Tani","doi":"10.1126/scirobotics.adp0751","DOIUrl":"10.1126/scirobotics.adp0751","url":null,"abstract":"<div >Humans excel at applying learned behavior to unlearned situations. A crucial component of this generalization behavior is our ability to compose/decompose a whole into reusable parts, an attribute known as compositionality. One of the fundamental questions in robotics concerns this characteristic: How can linguistic compositionality be developed concomitantly with sensorimotor skills through associative learning, particularly when individuals only learn partial linguistic compositions and their corresponding sensorimotor patterns? To address this question, we propose a brain-inspired neural network model that integrates vision, proprioception, and language into a framework of predictive coding and active inference on the basis of the free-energy principle. The effectiveness and capabilities of this model were assessed through various simulation experiments conducted with a robot arm. Our results show that generalization in learning to unlearned verb-noun compositions is significantly enhanced when training variations of task composition are increased. We attribute this to self-organized compositional structures in linguistic latent state space being influenced substantially by sensorimotor learning. Ablation studies show that visual attention and working memory are essential to accurately generate visuomotor sequences to achieve linguistically represented goals. These insights advance our understanding of mechanisms underlying development of compositionality through interactions of linguistic and sensorimotor experience.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 98","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020311","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}
Pub Date : 2025-01-22DOI: 10.1126/scirobotics.adp2356
Ignacio Abadía, Alice Bruel, Grégoire Courtine, Auke J. Ijspeert, Eduardo Ros, Niceto R. Luque
Robots have to adjust their motor behavior to changing environments and variable task requirements to successfully operate in the real world and physically interact with humans. Thus, robotics strives to enable a broad spectrum of adjustable motor behavior, aiming to mimic the human ability to function in unstructured scenarios. In humans, motor behavior arises from the integrative action of the central nervous system and body biomechanics; motion must be understood from a neuromechanics perspective. Nervous regions such as the cerebellum facilitate learning, adaptation, and coordination of our motor responses, ultimately driven by muscle activation. Muscles, in turn, self-stabilize motion through mechanical viscoelasticity. In addition, the agonist-antagonist arrangement of muscles surrounding joints enables cocontraction, which can be regulated to enhance motion accuracy and adapt joint stiffness, thereby providing impedance modulation and broadening the motor repertoire. Here, we propose a control solution that harnesses neuromechanics to enable adjustable robot motor behavior. Our solution integrates a muscle model that replicates mechanical viscoelasticity and cocontraction together with a cerebellar network providing motor adaptation. The resulting cerebello-muscular controller drives the robot through torque commands in a feedback control loop. Changes in cocontraction modify the muscle dynamics, and the cerebellum provides motor adaptation without relying on prior analytical solutions, driving the robot in different motor tasks, including payload perturbations and operation across unknown terrains. Experimental results show that cocontraction modulates robot stiffness, performance accuracy, and robustness against external perturbations. Through cocontraction modulation, our cerebello-muscular torque controller enables a broad spectrum of robot motor behavior.
{"title":"A neuromechanics solution for adjustable robot compliance and accuracy","authors":"Ignacio Abadía, Alice Bruel, Grégoire Courtine, Auke J. Ijspeert, Eduardo Ros, Niceto R. Luque","doi":"10.1126/scirobotics.adp2356","DOIUrl":"10.1126/scirobotics.adp2356","url":null,"abstract":"<div >Robots have to adjust their motor behavior to changing environments and variable task requirements to successfully operate in the real world and physically interact with humans. Thus, robotics strives to enable a broad spectrum of adjustable motor behavior, aiming to mimic the human ability to function in unstructured scenarios. In humans, motor behavior arises from the integrative action of the central nervous system and body biomechanics; motion must be understood from a neuromechanics perspective. Nervous regions such as the cerebellum facilitate learning, adaptation, and coordination of our motor responses, ultimately driven by muscle activation. Muscles, in turn, self-stabilize motion through mechanical viscoelasticity. In addition, the agonist-antagonist arrangement of muscles surrounding joints enables cocontraction, which can be regulated to enhance motion accuracy and adapt joint stiffness, thereby providing impedance modulation and broadening the motor repertoire. Here, we propose a control solution that harnesses neuromechanics to enable adjustable robot motor behavior. Our solution integrates a muscle model that replicates mechanical viscoelasticity and cocontraction together with a cerebellar network providing motor adaptation. The resulting cerebello-muscular controller drives the robot through torque commands in a feedback control loop. Changes in cocontraction modify the muscle dynamics, and the cerebellum provides motor adaptation without relying on prior analytical solutions, driving the robot in different motor tasks, including payload perturbations and operation across unknown terrains. Experimental results show that cocontraction modulates robot stiffness, performance accuracy, and robustness against external perturbations. Through cocontraction modulation, our cerebello-muscular torque controller enables a broad spectrum of robot motor behavior.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 98","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020312","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}
Pub Date : 2025-01-22DOI: 10.1126/scirobotics.adv3128
Robin R. Murphy
“Sunny,” the new Apple TV series, explores what happens if robot assistants develop emotions.
{"title":"Would a robot ever get angry enough to attack a person?","authors":"Robin R. Murphy","doi":"10.1126/scirobotics.adv3128","DOIUrl":"10.1126/scirobotics.adv3128","url":null,"abstract":"<div >“Sunny,” the new Apple TV series, explores what happens if robot assistants develop emotions.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 98","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025804","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}
For trained individuals such as athletes and musicians, learning often plateaus after extensive training, known as the “ceiling effect.” One bottleneck to overcome it is having no prior physical experience with the skill to be learned. Here, we challenge this issue by exposing expert pianists to fast and complex finger movements that cannot be performed voluntarily, using a hand exoskeleton robot that can move individual fingers quickly and independently. Although the skill of moving the fingers quickly plateaued through weeks of piano practice, passive exposure to otherwise impossible complex finger movements generated by the exoskeleton robot at a speed faster than the pianists’ fastest one enabled them to play faster. Neither a training for fast but simple finger movements nor one for slow but complex movements with the exoskeleton enhanced the overtrained motor skill. The exoskeleton training with one hand also improved the motor skill of the untrained contralateral hand, demonstrating the intermanual transfer effect. The training altered patterns of coordinated activities across multiple finger muscles during piano playing but not in general motor and somatosensory functions or in anatomical characteristics of the hand (range of motion). Patterns of the multifinger movements evoked by transcranial magnetic stimulation over the left motor cortex were also changed through passive exposure to fast and complex finger movements, which accompanied increased involvement of constituent movement elements characterizing the individuated finger movements. The results demonstrate evidence that somatosensory exposure to an unexperienced motor skill allows surmounting of the ceiling effect in a task-specific but effector-independent manner.
{"title":"Surmounting the ceiling effect of motor expertise by novel sensory experience with a hand exoskeleton","authors":"Shinichi Furuya, Takanori Oku, Hayato Nishioka, Masato Hirano","doi":"10.1126/scirobotics.adn3802","DOIUrl":"10.1126/scirobotics.adn3802","url":null,"abstract":"<div >For trained individuals such as athletes and musicians, learning often plateaus after extensive training, known as the “ceiling effect.” One bottleneck to overcome it is having no prior physical experience with the skill to be learned. Here, we challenge this issue by exposing expert pianists to fast and complex finger movements that cannot be performed voluntarily, using a hand exoskeleton robot that can move individual fingers quickly and independently. Although the skill of moving the fingers quickly plateaued through weeks of piano practice, passive exposure to otherwise impossible complex finger movements generated by the exoskeleton robot at a speed faster than the pianists’ fastest one enabled them to play faster. Neither a training for fast but simple finger movements nor one for slow but complex movements with the exoskeleton enhanced the overtrained motor skill. The exoskeleton training with one hand also improved the motor skill of the untrained contralateral hand, demonstrating the intermanual transfer effect. The training altered patterns of coordinated activities across multiple finger muscles during piano playing but not in general motor and somatosensory functions or in anatomical characteristics of the hand (range of motion). Patterns of the multifinger movements evoked by transcranial magnetic stimulation over the left motor cortex were also changed through passive exposure to fast and complex finger movements, which accompanied increased involvement of constituent movement elements characterizing the individuated finger movements. The results demonstrate evidence that somatosensory exposure to an unexperienced motor skill allows surmounting of the ceiling effect in a task-specific but effector-independent manner.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 98","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/scirobotics.adn3802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986388","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}
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