IEEE Transactions on Robotics最新文献

{"title":"Monolithic Programmable Fabric-stacking Enables Multifunctional Soft Robots","authors":"Jiaxi Wu, Mingxin Wu, Chen Wang, Guangming Xie","doi":"10.1109/tro.2025.3593118","DOIUrl":"https://doi.org/10.1109/tro.2025.3593118","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"111 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144737080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D-Printable Crease-Free Origami Vacuum Bending Actuators for Soft Robots","authors":"Zhanwei Wang;Huaijin Chen;Syeda Shadab Zehra Zaidi;Ellen Roels;Hendrik Cools;Bram Vanderborght;Seppe Terryn","doi":"10.1109/TRO.2025.3588726","DOIUrl":"10.1109/TRO.2025.3588726","url":null,"abstract":"While vacuum-based bending actuation offers benefits such as safety and compactness in soft robotics, it is often overlooked due to its limited actuation pressure, which restricts both bending angle and force output. This study presents a crease-free, origami-inspired vacuum bending actuator that advances both state-of-the-art vacuum bending actuators and traditional origami deformation principles by introducing orderly self folding through optimized stiffness distribution. Achieved through finite element method, this design provides several advantages: 1) Self-folding allows for high bending angles (up to 138<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>) in a compact form. 2) The crease-free design facilitates 3-D printing from a single soft material using a consumer-level fused filament fabrication printer, specifically thermoplastic polyurethane with a Shore hardness of 60 A, potentially higher flexibility and durability. 3) The compact configuration enables modular design, supporting reconfiguration as demonstrated in adaptable locomotion soft robots. 4) The large bending angles allow the actuator to wrap around objects, offering extensive contact compared to other designs. This capability, combined with its vacuum-driven mechanism, enables synergy with self-closing suction cups in an octopus-like vacuum gripper, providing large versatility and grasping force for handling a wide range of objects, from small, irregular shapes to larger, flat items.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"485-499"},"PeriodicalIF":10.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-motorized Hand Exoskeleton for Rescue and Beyond: Substantially Elevating Grip Endurance and Strength","authors":"Xianlong Mai, Jian Yang, Lei Li, Bin Zi, Shiwu Zhang, Xinglong Gong, Weihua Li, Guolin Yun, Shuaishuai Sun","doi":"10.1109/tro.2025.3588750","DOIUrl":"https://doi.org/10.1109/tro.2025.3588750","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"34 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative Design of Multi-functional Supernumerary Robotic Limbs with Ellipsoid Workspace Optimization","authors":"Jun Huo, Jian Huang, Jie Zuo, Bo Yang, Zhongzheng Fu, Xi Li, Samer Mohammed","doi":"10.1109/tro.2025.3588763","DOIUrl":"https://doi.org/10.1109/tro.2025.3588763","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"3 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multilevel Similarity Approach for Single-View Object Grasping: Matching, Planning, and Fine-Tuning","authors":"Hao Chen;Takuya Kiyokawa;Zhengtao Hu;Weiwei Wan;Kensuke Harada","doi":"10.1109/TRO.2025.3588720","DOIUrl":"10.1109/TRO.2025.3588720","url":null,"abstract":"Grasping unknown objects from a single view has remained a challenging topic in robotics due to the uncertainty of partial observation. Recent advances in large-scale models have led to benchmark solutions such as GraspNet-1Billion. However, such learning-based approaches still face a critical limitation in performance robustness for their sensitivity to sensing noise and environmental changes. To address this bottleneck in achieving highly generalized grasping, we abandon the traditional learning framework and introduce a new perspective: similarity matching, where similar known objects are utilized to guide the grasping of unknown target objects. We newly propose a method that robustly achieves unknown-object grasping from a single viewpoint through three key steps: 1) leverage the visual features of the observed object to perform similarity matching with an existing database containing various object models, identifying potential candidates with high similarity; 2) use the candidate models with pre-existing grasping knowledge to plan imitative grasps for the unknown target object; 3) optimize the grasp quality through a local fine-tuning process. To address the uncertainty caused by partial and noisy observation, we propose a multilevel similarity matching framework that integrates semantic, geometric, and dimensional features for comprehensive evaluation. Especially, we introduce a novel point cloud geometric descriptor, the clustered fast point feature histogram descriptor, which facilitates accurate similarity assessment between partial point clouds of observed objects and complete point clouds of database models. In addition, we incorporate the use of large language models, introduce the semioriented bounding box, and develop a novel point cloud registration approach based on plane detection to enhance matching accuracy under single-view conditions. Real-world experiments demonstrate that our proposed method significantly outperforms existing benchmarks in grasping a wide variety of unknown objects in both isolated and cluttered scenarios, showcasing exceptional robustness across varying object types and operating environments.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"500-519"},"PeriodicalIF":10.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144630006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"C$^*$: A New Bounding Approach for the Moving-Target Traveling Salesman Problem","authors":"Allen George Philip, Zhongqiang Ren, Sivakumar Rathinam, Howie Choset","doi":"10.1109/tro.2025.3588754","DOIUrl":"https://doi.org/10.1109/tro.2025.3588754","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"670 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144630007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CURL-SLAM: Continuous and Compact LiDAR Mapping","authors":"Kaicheng Zhang;Shida Xu;Yining Ding;Xianwen Kong;Sen Wang","doi":"10.1109/TRO.2025.3588442","DOIUrl":"10.1109/TRO.2025.3588442","url":null,"abstract":"This article studies 3-D light detection and ranging (LiDAR) mapping with a focus on developing an updatable and localizable map representation that enables continuity, compactness, and consistency in 3-D maps. Traditional LiDAR simultaneous localization and mapping (SLAM) systems often rely on 3-D point cloud maps, which typically require extensive storage to preserve structural details in large-scale environments. In this article, we propose a novel paradigm for LiDAR SLAM by leveraging the continuous and ultracompact representation of LiDAR (CURL). Our proposed LiDAR mapping approach, CURL-SLAM, produces compact 3-D maps capable of continuous reconstruction at variable densities using CURL’s spherical harmonics implicit encoding, and achieves global map consistency after loop closure. Unlike popular iterative-closest-point-based LiDAR odometry techniques, CURL-SLAM formulates LiDAR pose estimation as a unique optimization problem tailored for CURL and extends it to local bundle adjustment, enabling simultaneous pose refinement and map correction. Experimental results demonstrate that CURL-SLAM achieves state of the art 3-D mapping quality and competitive LiDAR trajectory accuracy, delivering sensor-rate real-time performance (10 Hz) on a CPU. We will release the CURL-SLAM implementation to the community.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"4538-4556"},"PeriodicalIF":10.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonrepetitive-Path Iterative Learning and Control for Human-Guided Robotic Operations on Unknown Surfaces","authors":"Kithmi N. D. Widanage;Jingkang Xia;Rizuwana Parween;Hareesh Godaba;Nicolas Herzig;Romeo Glovnea;Deqing Huang;Yanan Li","doi":"10.1109/TRO.2025.3588453","DOIUrl":"10.1109/TRO.2025.3588453","url":null,"abstract":"Automation of abrasive machining operations has become a challenging aspect in the remanufacturing industry where it is required to conduct operations on a surface of which the exact dimensions are unknown. In such cases, skilled human workers have to step in to perform labor-intensive tasks with inconsistent quality. In existing research work, collaborative robots are used to partially automate such operations under human supervision. However, these methods do not perform learning and control simultaneously and are often affected by the interactions of the human operator. In this article, a novel learning and control scheme is proposed where the robot explores an unknown surface iteratively while achieving the desired contact control performance under supervision and occasional interference from the human operator. The unknown surface is divided into subregions, and the learning and control parameters are updated each time the robot visits each subregion. This method is independent of the path of the robot and, thus, is unaffected by the irregularities introduced by a human operator’s interactions. The proposed method is applied to force control, stiffness learning, and orientation adaptation cases. The validity of this method is shown via simulations as well as experiments conducted using a Kinova Gen3 7-degrees of freedom robot.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"4922-4940"},"PeriodicalIF":10.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Baseline Policy Adapting and Abstraction of Shared Autonomy for High-Level Robot Operations","authors":"Ehsan Yousefi;Mo Chen;Inna Sharf","doi":"10.1109/TRO.2025.3588455","DOIUrl":"10.1109/TRO.2025.3588455","url":null,"abstract":"This article presents a novel shared autonomy and baseline policy adapting framework for human–robot interactions in high-level context-aware robotic tasks. With a unique methodology that leverages hierarchies in decision-making as well as variational analysis of human policy, we propose a mathematical model of shared autonomy policy. The framework aims at interpretable high-level decision-making for efficient robot operation with human in the loop. We modeled the decision-making process using hierarchical Markov decision processes in an algorithm we called <italic>policy adapting</i>, where the autonomous system policy is adapted, and hence shaped by incorporating design variables contextual to the robot, human, task, and pretraining. By integrating deep reinforcement learning within a multiagent hierarchical context, we present an end-to-end algorithm to train a baseline policy designed for shared autonomy. We showcase the effectiveness of our framework, and particularly the interplay between different design elements and human’s skill level, in a pilot study with a human user in a simulated sequence of high-level pick-and-place tasks. The proposed framework advances the state of the art in shared autonomy for robotic tasks, but can also be applied to other domains of autonomous operation.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"4574-4587"},"PeriodicalIF":10.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-to-End 2D-3D Registration Between Image and LiDAR Point Cloud for Vehicle Localization","authors":"Guangming Wang;Yu Zheng;Yuxuan Wu;Yanfeng Guo;Zhe Liu;Yixiang Zhu;Wolfram Burgard;Hesheng Wang","doi":"10.1109/TRO.2025.3588454","DOIUrl":"10.1109/TRO.2025.3588454","url":null,"abstract":"Robot localization using a built map is essential for a variety of tasks including accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization. However, the recent works for image-to-point cloud registration either divide the registration into separate modules or project the point cloud to the depth image to register the RGB and depth images. In this article, we present I2PNet, a novel end-to-end 2D-3D registration network, which directly registers the raw 3-D point cloud with the 2-D RGB image using differential modules with a united target. The 2D-3D cost volume module for differential 2D-3D association is proposed to bridge feature extraction and pose regression. The soft point-to-pixel correspondence is implicitly constructed on the intrinsic-independent normalized plane in the 2D-3D cost volume module. Moreover, we introduce an outlier mask prediction module to filter the outliers in the 2D-3D association before pose regression. Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to increase localization accuracy. Extensive localization experiments are conducted on the KITTI, nuScenes, M2DGR, Argoverse, Waymo, and Lyft5 datasets. The results demonstrate that I2PNet outperforms the state-of-the-art by a large margin and has a higher efficiency than the previous works. Moreover, we extend the application of I2PNet to the camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent approaches on the online calibration task.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"4643-4662"},"PeriodicalIF":10.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144611150","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}