Pub Date : 2025-06-25DOI: 10.1126/scirobotics.adt1591
Jack R. Williams, Chance F. Cuddeback, Shanpu Fang, Daniel Colley, Noah Enlow, Payton Cox, Paul Pridham, Zachary F. Lerner
Although the field of wearable robotic exoskeletons is rapidly expanding, there are several barriers to entry that discourage many from pursuing research in this area, ultimately hindering growth. Chief among these is the lengthy and costly development process to get an exoskeleton from conception to implementation and the necessity for a broad set of expertise. In addition, many exoskeletons are designed for a specific utility and are confined to the laboratory environment, limiting the flexibility of the designed system to adapt to answer new questions and explore new domains. To address these barriers, we present OpenExo, an open-source modular untethered exoskeleton framework that provides access to all aspects of the design process, including software, electronics, hardware, and control schemes. To demonstrate the utility of this exoskeleton framework, we performed benchtop and experimental validation testing with the system across multiple configurations, including hip-only incline assistance, ankle-only indoor and outdoor assistance, hip-and-ankle load carriage assistance, and elbow-only weightlifting assistance. All aspects of the software architecture, electrical components, hip and Bowden-cable transmission designs, and control schemes are freely available for other researchers to access, use, and modify when looking to address research questions in the field of wearable exoskeletons. Our hope is that OpenExo will accelerate the development and testing of new exoskeleton designs and control schemes while simultaneously encouraging others, including those who would have been turned away from entering the field, to explore new and unique research questions.
{"title":"OpenExo: An open-source modular exoskeleton to augment human function","authors":"Jack R. Williams, Chance F. Cuddeback, Shanpu Fang, Daniel Colley, Noah Enlow, Payton Cox, Paul Pridham, Zachary F. Lerner","doi":"10.1126/scirobotics.adt1591","DOIUrl":"10.1126/scirobotics.adt1591","url":null,"abstract":"<div >Although the field of wearable robotic exoskeletons is rapidly expanding, there are several barriers to entry that discourage many from pursuing research in this area, ultimately hindering growth. Chief among these is the lengthy and costly development process to get an exoskeleton from conception to implementation and the necessity for a broad set of expertise. In addition, many exoskeletons are designed for a specific utility and are confined to the laboratory environment, limiting the flexibility of the designed system to adapt to answer new questions and explore new domains. To address these barriers, we present OpenExo, an open-source modular untethered exoskeleton framework that provides access to all aspects of the design process, including software, electronics, hardware, and control schemes. To demonstrate the utility of this exoskeleton framework, we performed benchtop and experimental validation testing with the system across multiple configurations, including hip-only incline assistance, ankle-only indoor and outdoor assistance, hip-and-ankle load carriage assistance, and elbow-only weightlifting assistance. All aspects of the software architecture, electrical components, hip and Bowden-cable transmission designs, and control schemes are freely available for other researchers to access, use, and modify when looking to address research questions in the field of wearable exoskeletons. Our hope is that OpenExo will accelerate the development and testing of new exoskeleton designs and control schemes while simultaneously encouraging others, including those who would have been turned away from entering the field, to explore new and unique research questions.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 103","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/scirobotics.adt1591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479003","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 : 2025-06-25DOI: 10.1126/scirobotics.adz2721
Robin R. Murphy
Death of the Author: A Novel imagines the influence of an experimental exoskeleton on a disabled author and her family.
作者之死：一部小说想象了实验性外骨骼对一位残疾作家及其家人的影响。
{"title":"The greatest challenge for prosthetics may be social, not neural, connections","authors":"Robin R. Murphy","doi":"10.1126/scirobotics.adz2721","DOIUrl":"10.1126/scirobotics.adz2721","url":null,"abstract":"<div ><i>Death of the Author: A Novel</i> imagines the influence of an experimental exoskeleton on a disabled author and her family.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 103","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479004","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 : 2025-06-25DOI: 10.1126/scirobotics.adt0720
Haidong Yu, Xurui Liu, Yabin Zhang, Jie Shen, Xijun Liu, Shubo Liu, Xiangyu Wang, Bonan Sun, Huihui Du, Lin Xu, Bingsuo Zou, Jianning Ding, Qingsong Xu, Li Zhang, Ben Wang
Microrobotic techniques are promising for treating biofilm infections located deep within the human body. However, the presence of highly viscous pus presents a formidable biological barrier, severely restricting targeted and minimally invasive treatments. In addition, conventional antibacterial agents exhibit limited payload integration with microrobotic systems, further compromising therapeutic efficiency. In this study, we propose a photocatalytic microrobot through a magnetically guided, optical fiber–assisted therapeutic platform specifically designed to treat bacterial infections in deep mucosal cavities. The microrobots comprising copper (Cu) single atom–doped bismuth oxoiodide (BiOI), termed CBMRs, can be guided and tracked by real-time x-ray imaging. Under external magnetic actuation, the illuminated region from the magnetically guided optical fiber synchronously follows the CBMR swarm, enabling effective antibacterial action at targeted infection sites. Upon continuous visible-light irradiation, the resultant photothermal effect substantially reduces the viscosity of pus on inflamed mucosal tissues, enhancing the penetration capability of the CBMR swarm by more than threefold compared with baseline conditions. Concurrently, atomic-level design of CBMRs facilitates robust generation of reactive oxygen species, enabling efficient biofilm disruption and reductions in bacterial viability. We validated the effectiveness of this integrated optical fiber–assisted microrobotic platform in a rabbit sinusitis model in vivo, demonstrating its potential for clinically relevant infection therapy.
{"title":"Photocatalytic microrobots for treating bacterial infections deep within sinuses","authors":"Haidong Yu, Xurui Liu, Yabin Zhang, Jie Shen, Xijun Liu, Shubo Liu, Xiangyu Wang, Bonan Sun, Huihui Du, Lin Xu, Bingsuo Zou, Jianning Ding, Qingsong Xu, Li Zhang, Ben Wang","doi":"10.1126/scirobotics.adt0720","DOIUrl":"10.1126/scirobotics.adt0720","url":null,"abstract":"<div >Microrobotic techniques are promising for treating biofilm infections located deep within the human body. However, the presence of highly viscous pus presents a formidable biological barrier, severely restricting targeted and minimally invasive treatments. In addition, conventional antibacterial agents exhibit limited payload integration with microrobotic systems, further compromising therapeutic efficiency. In this study, we propose a photocatalytic microrobot through a magnetically guided, optical fiber–assisted therapeutic platform specifically designed to treat bacterial infections in deep mucosal cavities. The microrobots comprising copper (Cu) single atom–doped bismuth oxoiodide (BiOI), termed CBMRs, can be guided and tracked by real-time x-ray imaging. Under external magnetic actuation, the illuminated region from the magnetically guided optical fiber synchronously follows the CBMR swarm, enabling effective antibacterial action at targeted infection sites. Upon continuous visible-light irradiation, the resultant photothermal effect substantially reduces the viscosity of pus on inflamed mucosal tissues, enhancing the penetration capability of the CBMR swarm by more than threefold compared with baseline conditions. Concurrently, atomic-level design of CBMRs facilitates robust generation of reactive oxygen species, enabling efficient biofilm disruption and reductions in bacterial viability. We validated the effectiveness of this integrated optical fiber–assisted microrobotic platform in a rabbit sinusitis model in vivo, demonstrating its potential for clinically relevant infection therapy.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 103","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479002","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 : 2025-06-18DOI: 10.1126/scirobotics.ads6314
Zhidi Yang, James L. Weida, Siyuan Shao, Brandon Reedel, Collin Shannon, Junlin Chen, Piyush Sheth, Jonathan B. Hopkins
Despite the devastating effects of pressure ulcers (PUs), little is understood about how they can be prevented using alternating-pressure (AP) mattresses. Such mattresses typically aim to minimize the pressures imparted while alternating between different states of pressure to prevent areas of tissue from being persistently occluded of blood flow. In this work, we built an actuator bed to study AP approaches and learned that AP mattresses should aim to increase—not decrease—peak pressures to a certain extent if such areas are to be minimized for effectively preventing PUs. In addition, we learned that such mattresses should aim to increase the difference between their loading and off-loading pressures. We identified optimal parameters from the study and used them to design an AP mattress made of compliant mechanisms that markedly reduce areas of persistent occlusion by exhibiting relatively high peak pressures that are periodically alternated with substantially lower off-loading pressures. The mattress’s performance was characterized and compared against a standard foam pad in its flat and raised configurations. The load required to actuate the mattress from one of its stable states of pressure to the other was also measured.
{"title":"Preventing pressure ulcers by increasing pressure: An unorthodox alternating-pressure mattress","authors":"Zhidi Yang, James L. Weida, Siyuan Shao, Brandon Reedel, Collin Shannon, Junlin Chen, Piyush Sheth, Jonathan B. Hopkins","doi":"10.1126/scirobotics.ads6314","DOIUrl":"10.1126/scirobotics.ads6314","url":null,"abstract":"<div >Despite the devastating effects of pressure ulcers (PUs), little is understood about how they can be prevented using alternating-pressure (AP) mattresses. Such mattresses typically aim to minimize the pressures imparted while alternating between different states of pressure to prevent areas of tissue from being persistently occluded of blood flow. In this work, we built an actuator bed to study AP approaches and learned that AP mattresses should aim to increase—not decrease—peak pressures to a certain extent if such areas are to be minimized for effectively preventing PUs. In addition, we learned that such mattresses should aim to increase the difference between their loading and off-loading pressures. We identified optimal parameters from the study and used them to design an AP mattress made of compliant mechanisms that markedly reduce areas of persistent occlusion by exhibiting relatively high peak pressures that are periodically alternated with substantially lower off-loading pressures. The mattress’s performance was characterized and compared against a standard foam pad in its flat and raised configurations. The load required to actuate the mattress from one of its stable states of pressure to the other was also measured.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 103","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/scirobotics.ads6314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315420","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}
The rapid development of autonomous robots has resulted in marked societal and economic benefits. However, enabling robots to navigate complex environments with human-like agility remains a formidable challenge. Unlike robots, humans excel at pathfinding because of their superior spatial awareness and their ability to leverage experience. Inspired by these observations, we designed a neural network to simulate the intuitive pathfinding abilities of humans, integrating global environmental information and previous experiences to identify feasible pathways. Experiments demonstrated that, unlike traditional algorithms whose efficiency deteriorates in complex settings, the proposed method maintains stable computational performance. To further enhance motion quality, we introduce a numerically stable spatiotemporal trajectory optimizer with a unique bilayer polynomial trajectory representation in flat space. This optimization leverages differential flatness to enhance efficiency and fundamentally eliminates singularities in the original problem, thereby robustly converging to continuous and feasible motion even in complex maneuvering scenarios. Our hierarchical motion planner, validated through large-scale maze experiments, combines front-end path planning with back-end trajectory refinement, achieving robust and efficient navigation. We anticipate that our planner will advance stable navigation for robots in complex environments, thereby propelling the progress of robotic autonomy.
{"title":"Hierarchically depicting vehicle trajectory with stability in complex environments","authors":"Zhichao Han, Mengze Tian, Zaitian Gongye, Donglai Xue, Jiaxi Xing, Qianhao Wang, Yuman Gao, Jingping Wang, Chao Xu, Fei Gao","doi":"10.1126/scirobotics.ads4551","DOIUrl":"10.1126/scirobotics.ads4551","url":null,"abstract":"<div >The rapid development of autonomous robots has resulted in marked societal and economic benefits. However, enabling robots to navigate complex environments with human-like agility remains a formidable challenge. Unlike robots, humans excel at pathfinding because of their superior spatial awareness and their ability to leverage experience. Inspired by these observations, we designed a neural network to simulate the intuitive pathfinding abilities of humans, integrating global environmental information and previous experiences to identify feasible pathways. Experiments demonstrated that, unlike traditional algorithms whose efficiency deteriorates in complex settings, the proposed method maintains stable computational performance. To further enhance motion quality, we introduce a numerically stable spatiotemporal trajectory optimizer with a unique bilayer polynomial trajectory representation in flat space. This optimization leverages differential flatness to enhance efficiency and fundamentally eliminates singularities in the original problem, thereby robustly converging to continuous and feasible motion even in complex maneuvering scenarios. Our hierarchical motion planner, validated through large-scale maze experiments, combines front-end path planning with back-end trajectory refinement, achieving robust and efficient navigation. We anticipate that our planner will advance stable navigation for robots in complex environments, thereby propelling the progress of robotic autonomy.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 103","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315435","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 : 2025-06-18DOI: 10.1126/scirobotics.ads3968
Adam D. Hines, Michael Milford, Tobias Fischer
Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge. Visual place recognition, a critical component for navigation, is often hampered by the high resource demands of conventional systems, making them unsuitable for small-scale robotic platforms, which still require accurate long-endurance localization. Although neuromorphic approaches offer potential for greater efficiency, real-time edge deployment remains constrained by the complexity of biorealistic networks. To overcome this challenge, fusion of hardware and algorithms is critical when using this specialized computing paradigm. Here, we demonstrate a neuromorphic localization system that performs competitive place recognition in up to 8 kilometers of traversal using models as small as 180 kilobytes with 44,000 parameters while consuming less than 8% of the energy required by conventional methods. Our system, locational encoding with neuromorphic systems (LENS), integrates spiking neural networks, an event-based dynamic vision sensor, and a neuromorphic processor within a single SynSense Speck chip, enabling real-time, energy-efficient localization on a hexapod robot. When compared with a benchmark place recognition method, sum of absolute differences, LENS performs comparably in overall precision. LENS represents an accurate fully neuromorphic localization system capable of large-scale, on-device deployment for energy-efficient robotic place recognition. Neuromorphic computing enables resource-constrained robots to perform energy-efficient, accurate localization.
{"title":"A compact neuromorphic system for ultra–energy-efficient, on-device robot localization","authors":"Adam D. Hines, Michael Milford, Tobias Fischer","doi":"10.1126/scirobotics.ads3968","DOIUrl":"10.1126/scirobotics.ads3968","url":null,"abstract":"<div >Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge. Visual place recognition, a critical component for navigation, is often hampered by the high resource demands of conventional systems, making them unsuitable for small-scale robotic platforms, which still require accurate long-endurance localization. Although neuromorphic approaches offer potential for greater efficiency, real-time edge deployment remains constrained by the complexity of biorealistic networks. To overcome this challenge, fusion of hardware and algorithms is critical when using this specialized computing paradigm. Here, we demonstrate a neuromorphic localization system that performs competitive place recognition in up to 8 kilometers of traversal using models as small as 180 kilobytes with 44,000 parameters while consuming less than 8% of the energy required by conventional methods. Our system, locational encoding with neuromorphic systems (LENS), integrates spiking neural networks, an event-based dynamic vision sensor, and a neuromorphic processor within a single SynSense Speck chip, enabling real-time, energy-efficient localization on a hexapod robot. When compared with a benchmark place recognition method, sum of absolute differences, LENS performs comparably in overall precision. LENS represents an accurate fully neuromorphic localization system capable of large-scale, on-device deployment for energy-efficient robotic place recognition. Neuromorphic computing enables resource-constrained robots to perform energy-efficient, accurate localization.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 103","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315424","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 : 2025-06-11DOI: 10.1126/scirobotics.adq2303
David Hardman, Thomas George Thuruthel, Fumiya Iida
The human skin can reliably capture a wide range of multimodal data over a large surface while providing a soft interface. Artificial technologies using microelectromechanical systems (MEMS) can emulate these biological functions but present numerous challenges in fabrication, delamination due to soft-rigid interfaces, and electrical interference. To address these difficulties, we present a single-layer multimodal sensory skin made using only a highly sensitive hydrogel membrane. Using electrical impedance tomography techniques, we accessed up to 863,040 conductive pathways across the membrane, allowing us to identify at least six distinct types of multimodal stimuli, including human touch, damage, multipoint insulated presses, and local heating. Through comprehensive physical testing, we demonstrate that the highly redundant and coupled sensory information from these pathways can be structured using data-driven techniques, selecting which pathways should be monitored for efficient multimodal perception. To demonstrate our approach’s versatility, we cast the hydrogel into the shape and size of an adult human hand. Using our information structuring strategy, we demonstrate the hand’s ability to predict environmental conditions, localize human touch, and generate proprioceptive data. Our framework addresses the challenge of physically extracting meaningful information in multimodal soft sensing, opening new directions for the information-led design of single-layer skins in sensitive systems.
{"title":"Multimodal information structuring with single-layer soft skins and high-density electrical impedance tomography","authors":"David Hardman, Thomas George Thuruthel, Fumiya Iida","doi":"10.1126/scirobotics.adq2303","DOIUrl":"10.1126/scirobotics.adq2303","url":null,"abstract":"<div >The human skin can reliably capture a wide range of multimodal data over a large surface while providing a soft interface. Artificial technologies using microelectromechanical systems (MEMS) can emulate these biological functions but present numerous challenges in fabrication, delamination due to soft-rigid interfaces, and electrical interference. To address these difficulties, we present a single-layer multimodal sensory skin made using only a highly sensitive hydrogel membrane. Using electrical impedance tomography techniques, we accessed up to 863,040 conductive pathways across the membrane, allowing us to identify at least six distinct types of multimodal stimuli, including human touch, damage, multipoint insulated presses, and local heating. Through comprehensive physical testing, we demonstrate that the highly redundant and coupled sensory information from these pathways can be structured using data-driven techniques, selecting which pathways should be monitored for efficient multimodal perception. To demonstrate our approach’s versatility, we cast the hydrogel into the shape and size of an adult human hand. Using our information structuring strategy, we demonstrate the hand’s ability to predict environmental conditions, localize human touch, and generate proprioceptive data. Our framework addresses the challenge of physically extracting meaningful information in multimodal soft sensing, opening new directions for the information-led design of single-layer skins in sensitive systems.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 103","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260353","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 : 2025-06-11DOI: 10.1126/scirobotics.adt5685
Jake J. Abbott
The serious global need for on-orbit servicing of satellites and remediation of space debris demands robotic solutions.
全球对卫星在轨服务和空间碎片修复的严重需求需要机器人解决方案。
{"title":"Roboticists are grappling with space debris","authors":"Jake J. Abbott","doi":"10.1126/scirobotics.adt5685","DOIUrl":"10.1126/scirobotics.adt5685","url":null,"abstract":"<div >The serious global need for on-orbit servicing of satellites and remediation of space debris demands robotic solutions.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 103","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264739","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 : 2025-06-11DOI: 10.1126/scirobotics.adu2394
Mehmet Mert İlman, Annika Huber, Anand K. Mishra, Sabyasachi Sen, Fumin Wang, Tiffany Lin, Georg Jander, Abraham D. Stroock, Robert F. Shepherd
Precision agriculture aims to increase crop yield while reducing the use of harmful chemicals, such as pesticides and excess fertilizer, using minimal, tailored interventions. However, these strategies are limited by factors such as sensor quality, which typically relies on visual plant expression, and the manual, destructive nature of many nonvisual measurement methods, including the Scholander pressure bomb. By automating more intimate interactions with foliage in vivo, it would be possible to inject chemical and biological probes that reveal more phenotypes—such as water stress in response to varying environmental conditions and visible gene expression to measure the success of gene engineering applications. To address this, we developed a soft robotic leaf gripper and stamping-injection method to improve foliar delivery of nanoscale synthetic and biological probes. This allows for nondestructive, in situ, multispecies applications. We used two probes: Agrobacterium tumefaciens carrying the RUBY gene as a reporter system for plant transformation and nanoparticle hydrogels for measuring leaf water potential (ψ). Our hourglass-shaped design enabled the gripper to exert higher forces with reduced radial expansion compared with conventional designs, achieving an injection success rate above 91%. Studies on sunflower (Helianthus annuus L.) and cotton (Gossypium hirsutum L.) showed that our method achieved an average 12-fold increase in infiltration areas, with substantially less leaf damage—3.6% in sunflower and none in cotton—compared with the needle-free syringe method. Enabling long periods of successful in vivo phenotyping on both species after precise and safe foliar delivery underscores the potential of the leaf gripper for robotic plant bioengineering.
{"title":"In situ foliar augmentation of multiple species for optical phenotyping and bioengineering using soft robotics","authors":"Mehmet Mert İlman, Annika Huber, Anand K. Mishra, Sabyasachi Sen, Fumin Wang, Tiffany Lin, Georg Jander, Abraham D. Stroock, Robert F. Shepherd","doi":"10.1126/scirobotics.adu2394","DOIUrl":"10.1126/scirobotics.adu2394","url":null,"abstract":"<div >Precision agriculture aims to increase crop yield while reducing the use of harmful chemicals, such as pesticides and excess fertilizer, using minimal, tailored interventions. However, these strategies are limited by factors such as sensor quality, which typically relies on visual plant expression, and the manual, destructive nature of many nonvisual measurement methods, including the Scholander pressure bomb. By automating more intimate interactions with foliage in vivo, it would be possible to inject chemical and biological probes that reveal more phenotypes—such as water stress in response to varying environmental conditions and visible gene expression to measure the success of gene engineering applications. To address this, we developed a soft robotic leaf gripper and stamping-injection method to improve foliar delivery of nanoscale synthetic and biological probes. This allows for nondestructive, in situ, multispecies applications. We used two probes: <i>Agrobacterium tumefaciens</i> carrying the <i>RUBY</i> gene as a reporter system for plant transformation and nanoparticle hydrogels for measuring leaf water potential (ψ). Our hourglass-shaped design enabled the gripper to exert higher forces with reduced radial expansion compared with conventional designs, achieving an injection success rate above 91%. Studies on sunflower (<i>Helianthus annuus</i> L.) and cotton (<i>Gossypium hirsutum</i> L.) showed that our method achieved an average 12-fold increase in infiltration areas, with substantially less leaf damage—3.6% in sunflower and none in cotton—compared with the needle-free syringe method. Enabling long periods of successful in vivo phenotyping on both species after precise and safe foliar delivery underscores the potential of the leaf gripper for robotic plant bioengineering.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 103","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/scirobotics.adu2394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260352","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 : 2025-05-28DOI: 10.1126/scirobotics.ads6192
Hyeongjun Kim, Hyunsik Oh, Jeongsoo Park, Yunho Kim, Donghoon Youm, Moonkyu Jung, Minho Lee, Jemin Hwangbo
High-speed legged navigation in discrete and geometrically complex environments is a challenging task because of the high–degree-of-freedom dynamics and long-horizon, nonconvex nature of the optimization problem. In this work, we propose a hierarchical navigation pipeline for legged robots that can traverse such environments at high speed. The proposed pipeline consists of a planner and tracker module. The planner module finds physically feasible foothold plans by sampling-based optimization with fast sequential filtering using heuristics and a neural network. Subsequently, rollouts are performed in a physics simulation to identify the best foothold plan regarding the engineered cost function and to confirm its physical consistency. This hierarchical planning module is computationally efficient and physically accurate at the same time. The tracker aims to accurately step on the target footholds from the planning module. During the training stage, the foothold target distribution is given by a generative model that is trained competitively with the tracker. This process ensures that the tracker is trained in an environment with the desired difficulty. The resulting tracker can overcome terrains that are more difficult than what the previous methods could manage. We demonstrated our approach using Raibo, our in-house dynamic quadruped robot. The results were dynamic and agile motions: Raibo is capable of running on vertical walls, jumping a 1.3-meter gap, running over stepping stones at 4 meters per second, and autonomously navigating on terrains full of 30° ramps, stairs, and boxes of various sizes.
{"title":"High-speed control and navigation for quadrupedal robots on complex and discrete terrain","authors":"Hyeongjun Kim, Hyunsik Oh, Jeongsoo Park, Yunho Kim, Donghoon Youm, Moonkyu Jung, Minho Lee, Jemin Hwangbo","doi":"10.1126/scirobotics.ads6192","DOIUrl":"10.1126/scirobotics.ads6192","url":null,"abstract":"<div >High-speed legged navigation in discrete and geometrically complex environments is a challenging task because of the high–degree-of-freedom dynamics and long-horizon, nonconvex nature of the optimization problem. In this work, we propose a hierarchical navigation pipeline for legged robots that can traverse such environments at high speed. The proposed pipeline consists of a planner and tracker module. The planner module finds physically feasible foothold plans by sampling-based optimization with fast sequential filtering using heuristics and a neural network. Subsequently, rollouts are performed in a physics simulation to identify the best foothold plan regarding the engineered cost function and to confirm its physical consistency. This hierarchical planning module is computationally efficient and physically accurate at the same time. The tracker aims to accurately step on the target footholds from the planning module. During the training stage, the foothold target distribution is given by a generative model that is trained competitively with the tracker. This process ensures that the tracker is trained in an environment with the desired difficulty. The resulting tracker can overcome terrains that are more difficult than what the previous methods could manage. We demonstrated our approach using Raibo, our in-house dynamic quadruped robot. The results were dynamic and agile motions: Raibo is capable of running on vertical walls, jumping a 1.3-meter gap, running over stepping stones at 4 meters per second, and autonomously navigating on terrains full of 30° ramps, stairs, and boxes of various sizes.</div>","PeriodicalId":56029,"journal":{"name":"Science Robotics","volume":"10 102","pages":""},"PeriodicalIF":26.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165427","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}
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