Rapid urbanization has increased demand for customized urban mobility, making on-demand services and robo-taxis central to future transportation. The efficiency of these systems hinges on real-time fleet coordination algorithms. This work accelerates the state-of-the-art high-capacity ridepooling framework by identifying its computational bottlenecks and introducing two complementary strategies: (i) a data-driven feasibility predictor that filters low-potential trips, and (ii) a graph-partitioning scheme that enables parallelizable trip generation. Using real-world Manhattan demand data, we show that the acceleration algorithms reduce the optimality gap by up to 27% under real-time constraints and cut empty travel time by up to 5%. These improvements translate into tangible economic and environmental benefits, advancing the scalability of high-capacity robo-taxi operations in dense urban settings.
{"title":"Accelerating High-Capacity Ridepooling in Robo-Taxi Systems","authors":"Xinling Li;Daniele Gammelli;Alex Wallar;Jinhua Zhao;Gioele Zardini","doi":"10.1109/LRA.2026.3653376","DOIUrl":"https://doi.org/10.1109/LRA.2026.3653376","url":null,"abstract":"Rapid urbanization has increased demand for customized urban mobility, making on-demand services and robo-taxis central to future transportation. The efficiency of these systems hinges on real-time fleet coordination algorithms. This work accelerates the state-of-the-art high-capacity ridepooling framework by identifying its computational bottlenecks and introducing two complementary strategies: (i) a data-driven feasibility predictor that filters low-potential trips, and (ii) a graph-partitioning scheme that enables parallelizable trip generation. Using real-world Manhattan demand data, we show that the acceleration algorithms reduce the optimality gap by up to 27% under real-time constraints and cut empty travel time by up to 5%. These improvements translate into tangible economic and environmental benefits, advancing the scalability of high-capacity robo-taxi operations in dense urban settings.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 3","pages":"2450-2457"},"PeriodicalIF":5.3,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-13DOI: 10.1109/LRA.2026.3653384
Xubo Luo;Zhaojin Li;Xue Wan;Wei Zhang;Leizheng Shu
Accurate and real-time 6-DoF localization is mission-critical for autonomous lunar landing, yet existing approaches remain limited: visual odometry (VO) drifts unboundedly, while map-based absolute localization fails in texture-sparse or low-light terrain. We introduce KANLoc, a monocular localization framework that tightly couples VO with a lightweight but robust absolute pose regressor. At its core is a Kolmogorov–Arnold Network (KAN) that learns the complex mapping from image features to map coordinates, producing sparse but highly reliable global pose anchors. These anchors are fused into a bundle adjustment framework, effectively canceling drift while retaining local motion precision. KANLoc delivers three key advances: (i) a KAN-based pose regressor that achieves high accuracy with remarkable parameter efficiency, (ii) a hybrid VO–absolute localization scheme that yields globally consistent real-time trajectories ($geq$15 FPS), and (iii) a tailored data augmentation strategy that improves robustness to sensor occlusion. On both realistic synthetic and real lunar landing datasets, KANLoc reduces average translation and rotation error by 32% and 45%, respectively, with per-trajectory gains of up to 45% /48%, outperforming strong baselines.
准确实时的六自由度定位对于自主登月至关重要，但现有的方法仍然存在局限性：视觉里程计（VO）无边界漂移，而基于地图的绝对定位在纹理稀疏或低光照地形中失败。我们介绍了KANLoc，这是一个单目定位框架，它将VO与轻量级但鲁棒的绝对姿态回归器紧密耦合。其核心是Kolmogorov-Arnold网络（KAN），该网络学习从图像特征到地图坐标的复杂映射，产生稀疏但高度可靠的全局姿态锚。这些锚融合成一个束调整框架，有效地消除漂移，同时保持局部运动精度。KANLoc提供了三个关键的进步：(i)基于kan的姿态回归器，以显着的参数效率实现高精度，（ii）混合vo -绝对定位方案，产生全球一致的实时轨迹（$geq$ 15 FPS），以及（iii）量身定制的数据增强策略，提高对传感器闭塞的鲁棒性。在现实合成和真实登月数据集上，KANLoc将平均平移和旋转误差降低了32% and 45%, respectively, with per-trajectory gains of up to 45% /48%, outperforming strong baselines.
{"title":"Learning to Anchor Visual Odometry: KAN-Based Pose Regression for Planetary Landing","authors":"Xubo Luo;Zhaojin Li;Xue Wan;Wei Zhang;Leizheng Shu","doi":"10.1109/LRA.2026.3653384","DOIUrl":"https://doi.org/10.1109/LRA.2026.3653384","url":null,"abstract":"Accurate and real-time 6-DoF localization is mission-critical for autonomous lunar landing, yet existing approaches remain limited: visual odometry (VO) drifts unboundedly, while map-based absolute localization fails in texture-sparse or low-light terrain. We introduce KANLoc, a monocular localization framework that tightly couples VO with a lightweight but robust absolute pose regressor. At its core is a Kolmogorov–Arnold Network (KAN) that learns the complex mapping from image features to map coordinates, producing sparse but highly reliable global pose anchors. These anchors are fused into a bundle adjustment framework, effectively canceling drift while retaining local motion precision. KANLoc delivers three key advances: (i) a KAN-based pose regressor that achieves high accuracy with remarkable parameter efficiency, (ii) a hybrid VO–absolute localization scheme that yields globally consistent real-time trajectories (<inline-formula><tex-math>$geq$</tex-math></inline-formula>15 FPS), and (iii) a tailored data augmentation strategy that improves robustness to sensor occlusion. On both realistic synthetic and real lunar landing datasets, KANLoc reduces average translation and rotation error by 32% and 45%, respectively, with per-trajectory gains of up to 45% /48%, outperforming strong baselines.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 3","pages":"3574-3581"},"PeriodicalIF":5.3,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-12DOI: 10.1109/LRA.2026.3653300
Lequn Fu;Xiao Li;Yibin Liu;Xiangan Zeng;Yibo Peng;Youjun Xiong;Shiqi Li
Achieving natural, robust, and energy-efficient locomotion remains a central challenge for humanoid control. While imitation learning enables robots to reproduce human-like behaviors, differences in morphology, actuation, and partial observability often limit direct motion replication. This work proposes a human-inspired reinforcement learning framework that integrates both implicit and explicit guidance. Implicit human motion priors, obtained through adversarial learning, provide style alignment with human data, while explicit biomechanical rewards encode characteristic gait principles to promote symmetry, stability, and adaptability. In addition, a history-based state estimator explicitly reconstructs base velocities from partial observations, mitigating observability gaps and enhancing robustness in real-world settings. To assess human-likeness, we introduce a tri-metric evaluation protocol covering gait symmetry, human–robot similarity, and energy efficiency. Extensive experiments demonstrate that the proposed approach produces locomotion that is not only robust and transferable across diverse terrains but also energy-efficient and recognizably human-like.
{"title":"Human-Inspired Adaptive Gait Learning for Humanoids Locomotion","authors":"Lequn Fu;Xiao Li;Yibin Liu;Xiangan Zeng;Yibo Peng;Youjun Xiong;Shiqi Li","doi":"10.1109/LRA.2026.3653300","DOIUrl":"https://doi.org/10.1109/LRA.2026.3653300","url":null,"abstract":"Achieving natural, robust, and energy-efficient locomotion remains a central challenge for humanoid control. While imitation learning enables robots to reproduce human-like behaviors, differences in morphology, actuation, and partial observability often limit direct motion replication. This work proposes a human-inspired reinforcement learning framework that integrates both implicit and explicit guidance. Implicit human motion priors, obtained through adversarial learning, provide style alignment with human data, while explicit biomechanical rewards encode characteristic gait principles to promote symmetry, stability, and adaptability. In addition, a history-based state estimator explicitly reconstructs base velocities from partial observations, mitigating observability gaps and enhancing robustness in real-world settings. To assess human-likeness, we introduce a tri-metric evaluation protocol covering gait symmetry, human–robot similarity, and energy efficiency. Extensive experiments demonstrate that the proposed approach produces locomotion that is not only robust and transferable across diverse terrains but also energy-efficient and recognizably human-like.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 3","pages":"2458-2465"},"PeriodicalIF":5.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vision-Language-Action (VLA) models excel at robotic tasks by leveraging large-scale 2D vision-language pretraining, but their reliance on RGB images limits spatial reasoning critical for real-world interaction. Retraining these models with 3D data is computationally prohibitive, while discarding existing 2D datasets wastes valuable resources. To bridge this gap, we propose PointVLA, a framework that enhances pre-trained VLAs with point cloud inputs without requiring retraining. Our method freezes the vanilla action expert and injects 3D features via a lightweight modular block. To identify the most effective way of integrating point cloud representations, we conduct a skip-block analysis to pinpoint less useful blocks in the vanilla action expert, ensuring that 3D features are injected only into these blocks—minimizing disruption to pre-trained representations. Extensive experiments demonstrate that PointVLA outperforms state-of-the-art 2D imitation learning methods, such as OpenVLA, Diffusion Policy and DexVLA, across both simulated and real-world robotic tasks. Specifically, we highlight several key advantages of PointVLA enabled by point cloud integration: (1) Few-shot multi-tasking, where PointVLA successfully performs four different tasks using only 20 demonstrations each; (2) Real-vs-photo discrimination, where PointVLA distinguishes real objects from their images, leveraging 3D world knowledge to improve safety and reliability; (3) Height adaptability, where unlike conventional 2D imitation learning methods, PointVLA enables robots to adapt to objects at varying table heights that were unseen in training data. Furthermore, PointVLA achieves strong performance in long-horizon tasks, such as picking and packing objects from a moving conveyor belt, showcasing its ability to generalize across complex, dynamic environments.
视觉-语言-行动（VLA）模型通过利用大规模的2D视觉语言预训练在机器人任务中表现出色，但它们对RGB图像的依赖限制了对现实世界交互至关重要的空间推理。用3D数据重新训练这些模型在计算上是禁止的，而丢弃现有的2D数据集浪费了宝贵的资源。为了弥补这一差距，我们提出了PointVLA，这是一个使用点云输入增强预训练vla而无需再训练的框架。我们的方法是冻结普通的动作专家，并通过轻量级模块块注入3D功能。为了确定整合点云表示的最有效方法，我们进行了跳过块分析，以确定vanilla动作专家中不太有用的块，确保3D特征仅注入到这些块中，从而最大限度地减少对预训练表示的干扰。大量实验表明，PointVLA在模拟和现实机器人任务中都优于最先进的2D模仿学习方法，如OpenVLA、Diffusion Policy和DexVLA。具体来说，我们强调了点云集成支持的PointVLA的几个关键优势：(1)少镜头多任务，其中PointVLA成功执行四个不同的任务，每个任务仅使用20个演示；(2) real -vs-photo - discrimination， PointVLA将真实物体与其图像区分开来，利用3D世界知识提高安全性和可靠性；(3)高度适应性，与传统的2D模仿学习方法不同，PointVLA使机器人能够适应不同桌子高度的物体，这些物体在训练数据中是看不见的。此外，PointVLA在长期任务中表现出色，例如从移动的传送带中挑选和包装物体，展示了其在复杂动态环境中的泛化能力。
{"title":"PointVLA: Injecting the 3D World Into Vision-Language-Action Models","authors":"Chengmeng Li;Junjie Wen;Yaxin Peng;Yan Peng;Yichen Zhu","doi":"10.1109/LRA.2026.3653303","DOIUrl":"https://doi.org/10.1109/LRA.2026.3653303","url":null,"abstract":"Vision-Language-Action (VLA) models excel at robotic tasks by leveraging large-scale 2D vision-language pretraining, but their reliance on RGB images limits spatial reasoning critical for real-world interaction. Retraining these models with 3D data is computationally prohibitive, while discarding existing 2D datasets wastes valuable resources. To bridge this gap, we propose PointVLA, a framework that enhances pre-trained VLAs with point cloud inputs without requiring retraining. Our method freezes the vanilla action expert and injects 3D features via a lightweight modular block. To identify the most effective way of integrating point cloud representations, we conduct a skip-block analysis to pinpoint less useful blocks in the vanilla action expert, ensuring that 3D features are injected only into these blocks—minimizing disruption to pre-trained representations. Extensive experiments demonstrate that PointVLA outperforms state-of-the-art 2D imitation learning methods, such as OpenVLA, Diffusion Policy and DexVLA, across both simulated and real-world robotic tasks. Specifically, we highlight several key advantages of PointVLA enabled by point cloud integration: (1) <bold>Few-shot multi-tasking</b>, where PointVLA successfully performs four different tasks using only 20 demonstrations each; (2) <bold>Real-vs-photo discrimination</b>, where PointVLA distinguishes real objects from their images, leveraging 3D world knowledge to improve safety and reliability; (3) <bold>Height adaptability</b>, where unlike conventional 2D imitation learning methods, PointVLA enables robots to adapt to objects at varying table heights that were unseen in training data. Furthermore, PointVLA achieves strong performance in long-horizon tasks, such as picking and packing objects from a moving conveyor belt, showcasing its ability to generalize across complex, dynamic environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 3","pages":"2506-2513"},"PeriodicalIF":5.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-12DOI: 10.1109/LRA.2026.3653388
Deepak Singh;Shreyas Khobragade;Nitin J. Sanket
Autonomous aerial navigation in absolute darkness is crucial for post-disaster search and rescue operations, which often occur from disaster-zone power outages. Yet, due to resource constraints, tiny aerial robots, perfectly suited for these operations, are unable to navigate in the darkness to find survivors safely. In this letter, we present an autonomous aerial robot for navigation in the dark by combining an Infra-Red (IR) monocular camera with a large-aperture coded lens and structured light without external infrastructure like GPS or motion-capture. Our approach obtains depth-dependent defocus cues (each structured light point appears as a pattern that is depth dependent), which acts as a strong prior for our AsterNet deep depth estimation model. The model is trained in simulation by generating data using a simple optical model and transfers directly to the real world without any fine-tuning or retraining. AsterNet runs onboard the robot at 20 Hz on an NVIDIA Jetson Orin$^{text{TM}}$ Nano. Furthermore, our network is robust to changes in the structured light pattern and relative placement of the pattern emitter and IR camera, leading to simplified and cost-effective construction. We successfully evaluate and demonstrate our proposed depth navigation approach AsterNav using depth from AsterNet in many real-world experiments using only onboard sensing and computation, including dark matte obstacles and thin ropes ($varnothing$ 6.25 mm), achieving an overall success rate of 95.5% with unknown object shapes, locations and materials. To the best of our knowledge, this is the first work on monocular, structured-light-based quadrotor navigation in absolute darkness.
{"title":"AsterNav: Autonomous Aerial Robot Navigation in Darkness Using Passive Computation","authors":"Deepak Singh;Shreyas Khobragade;Nitin J. Sanket","doi":"10.1109/LRA.2026.3653388","DOIUrl":"https://doi.org/10.1109/LRA.2026.3653388","url":null,"abstract":"Autonomous aerial navigation in absolute darkness is crucial for post-disaster search and rescue operations, which often occur from disaster-zone power outages. Yet, due to resource constraints, tiny aerial robots, perfectly suited for these operations, are unable to navigate in the darkness to find survivors safely. In this letter, we present an autonomous aerial robot for navigation in the dark by combining an Infra-Red (IR) monocular camera with a large-aperture coded lens and structured light without external infrastructure like GPS or motion-capture. Our approach obtains depth-dependent defocus cues (each structured light point appears as a pattern that is depth dependent), which acts as a strong prior for our <italic>AsterNet</i> deep depth estimation model. The model is trained in simulation by generating data using a simple optical model and transfers directly to the real world without any fine-tuning or retraining. <italic>AsterNet</i> runs onboard the robot at 20 Hz on an NVIDIA Jetson Orin<inline-formula><tex-math>$^{text{TM}}$</tex-math></inline-formula> Nano. Furthermore, our network is robust to changes in the structured light pattern and relative placement of the pattern emitter and IR camera, leading to simplified and cost-effective construction. We successfully evaluate and demonstrate our proposed depth navigation approach <italic>AsterNav</i> using depth from <italic>AsterNet</i> in many real-world experiments using only onboard sensing and computation, including dark matte obstacles and thin ropes (<inline-formula><tex-math>$varnothing$</tex-math></inline-formula> 6.25 mm), achieving an overall success rate of <italic>95.5%</i> with unknown object shapes, locations and materials. To the best of our knowledge, this is the first work on monocular, structured-light-based quadrotor navigation in absolute darkness.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 3","pages":"3907-3914"},"PeriodicalIF":5.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-12DOI: 10.1109/LRA.2026.3653287
Yizhi Zhou;Yufan Liu;Xuan Wang
This letter studies the problem of Cooperative Localization (CL) for multi-robot systems in 3-D environments, where a group of mobile robots jointly localize themselves by using measurements from onboard sensors and shared information from other robots. To ensure the efficiency of information fusion and observability consistency in a distributed CL system, we propose a distributed multi-robot CL method based on Lie groups, well-suited for 3-D scenarios with full 3-D rotational dynamics and generic nonlinear inter-robot measurement models. Unlike most existing distributed CL algorithms that operate in vector space and are only applicable to simple 2-D environments, the proposed algorithm performs distributed information fusion directly on the manifold that inherently accounts for the non-Euclidean nature of 3-D rotations and translations. By leveraging the nice property of invariant errors, we analytically prove that the proposed algorithm naturally preserves the observability consistency of the CL system. This ensures that the system maintains the correct structure of unobservable directions throughout the estimation process. The effectiveness of the proposed algorithm is validated by several numerical experiments conducted to rigorously investigate the effects of relative information fusion in the distributed CL system.
{"title":"Distributed 3-D Multi-Robot Cooperative Localization: An Efficient and Consistent Approach","authors":"Yizhi Zhou;Yufan Liu;Xuan Wang","doi":"10.1109/LRA.2026.3653287","DOIUrl":"https://doi.org/10.1109/LRA.2026.3653287","url":null,"abstract":"This letter studies the problem of Cooperative Localization (CL) for multi-robot systems in 3-D environments, where a group of mobile robots jointly localize themselves by using measurements from onboard sensors and shared information from other robots. To ensure the efficiency of information fusion and observability consistency in a distributed CL system, we propose a distributed multi-robot CL method based on Lie groups, well-suited for 3-D scenarios with full 3-D rotational dynamics and generic nonlinear inter-robot measurement models. Unlike most existing distributed CL algorithms that operate in vector space and are only applicable to simple 2-D environments, the proposed algorithm performs distributed information fusion directly on the manifold that inherently accounts for the non-Euclidean nature of 3-D rotations and translations. By leveraging the nice property of invariant errors, we analytically prove that the proposed algorithm naturally preserves the observability consistency of the CL system. This ensures that the system maintains the correct structure of unobservable directions throughout the estimation process. The effectiveness of the proposed algorithm is validated by several numerical experiments conducted to rigorously investigate the effects of relative information fusion in the distributed CL system.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 2","pages":"2306-2313"},"PeriodicalIF":5.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although fully autonomous systems still face challenges due to patients' anatomical variability, teleoperated systems appear to be more practical in current healthcare settings. This paper presents an anatomy-aware control framework for teleoperated lung ultrasound. Leveraging biomechanically accurate 3D modelling, the system applies virtual constraints on the ultrasound probe pose and provides real-time visual feedback to assist in precise probe placement tasks. A twofold evaluation, one with 5 naïve operators on a single volunteer and the second with a single experienced operator on 6 volunteers, compared our method with a standard teleoperation baseline. The results of the first one characterised the accuracy of the anatomical model and the improved perceived performance by the naïve operators, while the second one focused on the efficiency of the system in improving probe placement and reducing procedure time compared to traditional teleoperation. The results demonstrate that the proposed framework enhances the physician's capabilities in executing remote lung ultrasound, reducing more than 20% of execution time on 4-point acquisitions, towards faster, more objective and repeatable exams.
{"title":"An Anatomy-Aware Shared Control Approach for Assisted Teleoperation of Lung Ultrasound Examinations","authors":"Davide Nardi;Edoardo Lamon;Daniele Fontanelli;Matteo Saveriano;Luigi Palopoli","doi":"10.1109/LRA.2026.3653292","DOIUrl":"https://doi.org/10.1109/LRA.2026.3653292","url":null,"abstract":"Although fully autonomous systems still face challenges due to patients' anatomical variability, teleoperated systems appear to be more practical in current healthcare settings. This paper presents an anatomy-aware control framework for teleoperated lung ultrasound. Leveraging biomechanically accurate 3D modelling, the system applies virtual constraints on the ultrasound probe pose and provides real-time visual feedback to assist in precise probe placement tasks. A twofold evaluation, one with 5 naïve operators on a single volunteer and the second with a single experienced operator on 6 volunteers, compared our method with a standard teleoperation baseline. The results of the first one characterised the accuracy of the anatomical model and the improved perceived performance by the naïve operators, while the second one focused on the efficiency of the system in improving probe placement and reducing procedure time compared to traditional teleoperation. The results demonstrate that the proposed framework enhances the physician's capabilities in executing remote lung ultrasound, reducing more than 20% of execution time on 4-point acquisitions, towards faster, more objective and repeatable exams.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 3","pages":"2570-2577"},"PeriodicalIF":5.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11346947","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-12DOI: 10.1109/LRA.2026.3653299
Saekwang Nam;Bowen Deng;Loong Yi Lee;Jonathan M. Rossiter;Nathan F. Lepora
We present a tactile-sensorized Fin-Ray finger that enables simultaneous detection of contact location and indentation depth through an indirect sensing approach. A hinge mechanism is integrated between the soft Fin-Ray structure and a rigid sensing module, allowing deformation and translation information to be transferred to a bottom crossbeam upon which are an array of marker-tipped pins based on the biomimetic structure of the TacTip vision-based tactile sensor. Deformation patterns captured by an internal camera are processed using a convolutional neural network to infer contact conditions without directly sensing the finger surface. The finger design was optimized by varying pin configurations and hinge orientations, achieving 0.1 mm depth and 2 mm location-sensing accuracies. The perception demonstrated robust generalization to various indenter shapes and sizes, which was applied to a pick-and-place task under uncertain picking positions, where the tactile feedback significantly improved placement accuracy. Overall, this work provides a lightweight, flexible, and scalable tactile sensing solution suitable for soft robotic structures where the sensing needs situating away from the contact interface.
{"title":"TacFinRay: Soft Tactile Fin-Ray Finger With Indirect Tactile Sensing for Robust Grasping","authors":"Saekwang Nam;Bowen Deng;Loong Yi Lee;Jonathan M. Rossiter;Nathan F. Lepora","doi":"10.1109/LRA.2026.3653299","DOIUrl":"https://doi.org/10.1109/LRA.2026.3653299","url":null,"abstract":"We present a tactile-sensorized Fin-Ray finger that enables simultaneous detection of contact location and indentation depth through an indirect sensing approach. A hinge mechanism is integrated between the soft Fin-Ray structure and a rigid sensing module, allowing deformation and translation information to be transferred to a bottom crossbeam upon which are an array of marker-tipped pins based on the biomimetic structure of the TacTip vision-based tactile sensor. Deformation patterns captured by an internal camera are processed using a convolutional neural network to infer contact conditions without directly sensing the finger surface. The finger design was optimized by varying pin configurations and hinge orientations, achieving 0.1 mm depth and 2 mm location-sensing accuracies. The perception demonstrated robust generalization to various indenter shapes and sizes, which was applied to a pick-and-place task under uncertain picking positions, where the tactile feedback significantly improved placement accuracy. Overall, this work provides a lightweight, flexible, and scalable tactile sensing solution suitable for soft robotic structures where the sensing needs situating away from the contact interface.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 3","pages":"2722-2729"},"PeriodicalIF":5.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-12DOI: 10.1109/LRA.2026.3653394
Jonghyeok Kim;Wan Kyun Chung
Among the many choices in the matrix-vector factorization of the Coriolis and centripetal terms satisfying the skew-symmetry condition in system dynamics, the unique factorization, called Christoffel-consistent (CC) factorization, has been proposed. We derived the unique CC factorization in the Lie group context and examined the impact of Christoffel inconsistency in Coriolis matrix factorization on the dynamic behavior of robot systems during both free motion and interaction with humans, particularly in the context of passivity-based controllers and augmented PD controllers. Specifically, the question is: What are the advantages of using the CC factorization, and what is the effect of non-CC factorization on the robot’s dynamic behavior, which has been rarely explored? We showed that Christoffel inconsistency generates unwanted torsion, causing the system to deviate from the desired trajectory, and this results in undesirable dynamic behavior when controlling the system, especially when the dynamics of the robot is described by twist and wrench. Through simulation and a real-world robot experiment, this phenomenon is verified for the first time.
{"title":"Christoffel-Consistent Coriolis Factorization and Its Effect on the Control of a Robot","authors":"Jonghyeok Kim;Wan Kyun Chung","doi":"10.1109/LRA.2026.3653394","DOIUrl":"https://doi.org/10.1109/LRA.2026.3653394","url":null,"abstract":"Among the many choices in the matrix-vector factorization of the Coriolis and centripetal terms satisfying the skew-symmetry condition in system dynamics, the unique factorization, called Christoffel-consistent (CC) factorization, has been proposed. We derived the unique CC factorization in the Lie group context and examined the impact of Christoffel inconsistency in Coriolis matrix factorization on the dynamic behavior of robot systems during both free motion and interaction with humans, particularly in the context of passivity-based controllers and augmented PD controllers. Specifically, the question is: What are the advantages of using the CC factorization, and what is the effect of non-CC factorization on the robot’s dynamic behavior, which has been rarely explored? We showed that Christoffel inconsistency generates unwanted torsion, causing the system to deviate from the desired trajectory, and this results in undesirable dynamic behavior when controlling the system, especially when the dynamics of the robot is described by twist and wrench. Through simulation and a real-world robot experiment, this phenomenon is verified for the first time.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 3","pages":"2682-2689"},"PeriodicalIF":5.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-12DOI: 10.1109/LRA.2026.3652069
Jianxi Zhang;Jingtian Zhang;Hong Zeng;Dapeng Chen;Huijun Li;Aiguo Song
The foreign objects on utility poles may damage power lines and cause significant disruptions in electricity supply. A widely used approach to address this issue is for qualified personnel to climb on the pole and remove the foreign objects in a timely manner using an insulating tube. However, prolonged overhead manipulation of the insulating tube in the constrained environment not only leads to considerable upper-limb fatigue but also makes accurate tube positioning increasingly challenging. To address these challenges, wearable robotic limbs with an active control strategy have the potential to effectively reduce upper-limb fatigue and assist in tube positioning. This work presents supernumerary robotic limbs (SRLs) designed to assist electrical workers in a simulated overhead foreign objects removal task. We further propose a shared control method based on finite-horizon non-zero-sum game theory. This method models the cooperation between the SRL and the worker to adaptively modulate the input of the SRL, thereby providing rapid and accurate assistance in tube positioning. Experimental results show that the proposed SRL can reduce primary upper-limb muscle activity (deltoid, biceps brachii, brachioradialis and flexor carpi radialis) by up to 59.73% compared with performing the task without the SRL. Moreover, compared with a method that ignores human input, the proposed control strategy achieves more accurate positioning during human-SRLs cooperation. These results demonstrate the potential of both the SRL and the control strategy for the live-line overhead foreign objects removal task.
{"title":"Development and Control of Supernumerary Robotic Limbs for Overhead Tube Manipulation Task","authors":"Jianxi Zhang;Jingtian Zhang;Hong Zeng;Dapeng Chen;Huijun Li;Aiguo Song","doi":"10.1109/LRA.2026.3652069","DOIUrl":"https://doi.org/10.1109/LRA.2026.3652069","url":null,"abstract":"The foreign objects on utility poles may damage power lines and cause significant disruptions in electricity supply. A widely used approach to address this issue is for qualified personnel to climb on the pole and remove the foreign objects in a timely manner using an insulating tube. However, prolonged overhead manipulation of the insulating tube in the constrained environment not only leads to considerable upper-limb fatigue but also makes accurate tube positioning increasingly challenging. To address these challenges, wearable robotic limbs with an active control strategy have the potential to effectively reduce upper-limb fatigue and assist in tube positioning. This work presents supernumerary robotic limbs (SRLs) designed to assist electrical workers in a simulated overhead foreign objects removal task. We further propose a shared control method based on finite-horizon non-zero-sum game theory. This method models the cooperation between the SRL and the worker to adaptively modulate the input of the SRL, thereby providing rapid and accurate assistance in tube positioning. Experimental results show that the proposed SRL can reduce primary upper-limb muscle activity (deltoid, biceps brachii, brachioradialis and flexor carpi radialis) by up to 59.73% compared with performing the task without the SRL. Moreover, compared with a method that ignores human input, the proposed control strategy achieves more accurate positioning during human-SRLs cooperation. These results demonstrate the potential of both the SRL and the control strategy for the live-line overhead foreign objects removal task.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 3","pages":"2634-2641"},"PeriodicalIF":5.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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