IEEE Transactions on Robotics最新文献_第9页

Visual-Tactile Grasp Dataset and Grasp Margin Matrix Analysis for Stability Evaluation 视觉触觉抓取数据集及抓取余量矩阵分析

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2026-01-01 Epub Date: 2026-03-10 DOI: 10.1109/TRO.2026.3672523

Wanhao Niu;Zifan Zhu;Jianxin Zheng;Chungang Zhuang

{"title":"Visual-Tactile Grasp Dataset and Grasp Margin Matrix Analysis for Stability Evaluation","authors":"Wanhao Niu;Zifan Zhu;Jianxin Zheng;Chungang Zhuang","doi":"10.1109/TRO.2026.3672523","DOIUrl":"10.1109/TRO.2026.3672523","url":null,"abstract":"Robotic grasping plays a critical role in robotics, with widespread applications across various domains. The stability of a grasp is crucial for subsequent operations, making accurate and robust stability assessments essential. While existing methods predominantly rely on visual data, the absence of tactile signals and quantitative stability metrics often leads to unreliable grasp execution. This article makes three fundamental contributions to address these limitations. First, a visual-tactile grasp dataset generation framework is proposed using NVIDIA Isaac Sim, which synthesizes large-scale multimodal grasping scenarios with stability degree labels through a physics-informed three-stage pipeline. Second, this article introduces the grasp margin matrix, a novel computational model that quantifies directional force margins and rotational moment margins to evaluate grasp robustness. This matrix simplifies traditional grasp wrench space analysis by decoupling complex friction cone calculations into interpretable mechanical metrics, achieving 87.72% stability classification accuracy via our stability assessment network. Third, a vision-based grasp perception network that predicts contact force distributions and object centroids without physical tactile sensors is developed, enabling real-time stability inference through the grasp margin matrix. This perception–evaluation–decision integration links grasp pose generation with stability assessment to inform grasp selection, achieving an 88.15% success rates in real robotic grasping scenarios. Experimental validations demonstrate significant improvements compared to force closure methods, with accuracy improving from 56.52% to 87.72%, offering both theoretical rigor and practical feasibility for industrial robotic systems.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"1515-1534"},"PeriodicalIF":10.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VLN-Game: Vision-Language Equilibrium Search for Zero-Shot Semantic Navigation VLN-Game：零射击语义导航的视觉语言平衡搜索

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2026-01-01 Epub Date: 2026-03-24 DOI: 10.1109/TRO.2026.3677047

Bangguo Yu;Yuzhen Liu;Lei Han;Hamidreza Kasaei;Tingguang Li;Ming Cao

{"title":"VLN-Game: Vision-Language Equilibrium Search for Zero-Shot Semantic Navigation","authors":"Bangguo Yu;Yuzhen Liu;Lei Han;Hamidreza Kasaei;Tingguang Li;Ming Cao","doi":"10.1109/TRO.2026.3677047","DOIUrl":"10.1109/TRO.2026.3677047","url":null,"abstract":"Following human instructions to explore and search for a specified target in an unfamiliar environment is a crucial skill for mobile service robots. Most of the previous works on object goal navigation have typically focused on a single input modality as the target, which may lead to limited consideration of language descriptions containing detailed attributes and spatial relationships. To address this limitation, we propose VLN-Game, a novel zero-shot framework for visual target navigation that can process object names and descriptive language targets effectively. To be more precise, our approach constructs a 3-D object-centric spatial map by integrating pretrained visual-language features with a 3-D reconstruction of the physical environment. Then, the framework identifies the most promising areas to explore in search of potential target candidates. A game-theoretic vision-language model is employed to determine which target best matches the given language description. Experiments conducted on the Habitat-Matterport 3-D dataset demonstrate that the proposed framework achieves state-of-the-art performance in both object goal navigation and language-based navigation tasks. Moreover, we show that VLN-Game can be easily deployed on real-world robots. The success of VLN-Game highlights the promising potential of using game-theoretic methods with compact vision-language models to advance decision-making capabilities in robotic systems.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"1824-1839"},"PeriodicalIF":10.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Homotopic Information Gain for Sparse Active Target Tracking 稀疏主动目标跟踪的同伦信息增益

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2026-01-01 Epub Date: 2026-03-10 DOI: 10.1109/TRO.2026.3672529

Jennifer Wakulicz;Ki Myung Brian Lee;Teresa Vidal-Calleja;Robert Fitch

引用次数: 0

Correspondence-Free, Function-Based Sim-to-Real Learning for Deformable Surface Control 可变形曲面控制的无对应、基于函数的模拟到真实学习

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-24 DOI: 10.1109/TRO.2025.3647769

Yingjun Tian;Guoxin Fang;Renbo Su;Aoran Lyu;Neelotpal Dutta;Weiming Wang;Simeon Gill;Andrew Weightman;Charlie C.L. Wang

引用次数: 0

Behavior-Controllable Stable Dynamics Models on Riemannian Configuration Manifolds 黎曼构型流形的行为可控稳定动力学模型

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-24 DOI: 10.1109/TRO.2025.3647763

Byeongho Lee;Yonghyeon Lee;Junsu Ha;Frank C. Park

{"title":"Behavior-Controllable Stable Dynamics Models on Riemannian Configuration Manifolds","authors":"Byeongho Lee;Yonghyeon Lee;Junsu Ha;Frank C. Park","doi":"10.1109/TRO.2025.3647763","DOIUrl":"10.1109/TRO.2025.3647763","url":null,"abstract":"Due to their stability and robustness properties, stable dynamical systems (SDSs) have received considerable attention as a means of representing motions in learning from demonstration tasks. Designing vector fields that fit complex trajectories while ensuring stability still remains a key challenge; although recent deep-learning-based methods have shown substantial progress in this direction, their tendency to overfit to demonstration trajectories often leads to undesirable behaviors, particularly as tasks deviate from demonstrations. At a fundamental level, the only reliable way to address this lack of generalization is to provide supervision in out-of-demonstration regions. Focusing on two types of general behaviors, mimicking and contracting, we propose a behavior-controllable stable dynamics model (BCSDM), a one-parameter family of SDS that allows users to adjust the system’s overall behavior depending on user intent. We show how to extend the BCSDM to accommodate demonstrations of multiple tasks, and also propose a deep operator vector field for memory-efficient encoding of multiple dynamical systems. Extensive experiments on tasks that involve mimicking or contracting behaviors demonstrate the advantages of BCSDMs over existing state-of-the-art SDS learning methods.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"713-733"},"PeriodicalIF":10.5,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robot Tracking Control With Natural Task-Space Decoupling 基于自然任务空间解耦的机器人跟踪控制

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-24 DOI: 10.1109/TRO.2025.3647777

Alexander Dietrich;Xuwei Wu;Maged Iskandar;Alin Albu-Schäffer

{"title":"Robot Tracking Control With Natural Task-Space Decoupling","authors":"Alexander Dietrich;Xuwei Wu;Maged Iskandar;Alin Albu-Schäffer","doi":"10.1109/TRO.2025.3647777","DOIUrl":"10.1109/TRO.2025.3647777","url":null,"abstract":"There exist numerous ways to achieve multitasking control in kinematically redundant robots to accomplish several goals simultaneously. In all approaches, regardless of the specific type of controller, one has to make a choice about the closed-loop inertia and consequently the dynamic task couplings. Here, we introduce a new control strategy that combines two fundamentally different properties that have not yet been brought together. First, we fully and dynamically decouple all individual subtasks, which cannot be achieved with classical passivity-based or hierarchical approaches. Second, we provide high robustness in practice, which is structurally not possible with any inverse dynamics approaches enforcing a decoupled but constant closed-loop inertia. Besides formal proofs of stability and passivity, we compare our approach with the other categories in various simulations and experiments. Since the proposed controller is grounded in the fundamental property of full natural task-space decoupling, this underlying strategy and its benefits can also be transferred to other design methods such as quadratic programming, model-predictive control, or learning-based approaches.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"734-749"},"PeriodicalIF":10.5,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PushingBots: Collaborative Pushing via Neural Accelerated Combinatorial Hybrid Optimization 推机器人：通过神经加速组合混合优化的协作推

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-24 DOI: 10.1109/TRO.2025.3647767

Zili Tang;Ying Zhang;Meng Guo

{"title":"PushingBots: Collaborative Pushing via Neural Accelerated Combinatorial Hybrid Optimization","authors":"Zili Tang;Ying Zhang;Meng Guo","doi":"10.1109/TRO.2025.3647767","DOIUrl":"10.1109/TRO.2025.3647767","url":null,"abstract":"Many robots are not equipped with a manipulator and many objects are not suitable for prehensile manipulation (such as large boxes and cylinders). In these cases, pushing is a simple yet effective nonprehensile skill for robots to interact with and further change the environment. Existing work often assumes a set of predefined pushing modes and fixed-shape objects. This work tackles the general problem of controlling a robotic fleet to push collaboratively numerous arbitrary objects to respective destinations, within complex environments of cluttered and movable obstacles. It incorporates several characteristic challenges for multirobot systems, such as online task coordination under large uncertainties of cost and duration, and for contact-rich tasks, such as hybrid switching among different contact modes, and under actuation due to constrained contact forces. The proposed method is based on combinatorial hybrid optimization over dynamic task assignments and hybrid execution via sequences of pushing modes and associated forces. It consists of the following three main components: first, the decomposition, ordering, and rolling assignment of pushing subtasks to robot subgroups; second, the keyframe guided hybrid search to optimize the sequence of parameterized pushing modes for each subtask; third, the hybrid control to execute these modes and transit among them. Last but not least, a diffusion-based accelerator is adopted to predict the keyframes and pushing modes that should be prioritized during hybrid search; and further improve planning efficiency. The framework is complete under mild assumptions. Its efficiency and effectiveness under different numbers of robots and general-shaped objects are validated extensively in simulations and hardware experiments, as well as generalizations to heterogeneous robots, planar assembly, and 6-D pushing.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"579-599"},"PeriodicalIF":10.5,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrections to “Characterizing Trust and Resilience in Distributed Consensus for Cyberphysical Systems” 更正“描述网络物理系统分布式共识中的信任和弹性”

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-18 DOI: 10.1109/TRO.2025.3645896

Michal Yemini;Angelia Nedić;Andrea J. Goldsmith;Stephanie Gil

引用次数: 0

A Multifingered Robotic Hand With Fiber-Optic Force and Tactile Sensing for Remote Manipulation 一种具有光纤力和触觉传感的多指机器人手用于远程操作

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-18 DOI: 10.1109/TRO.2025.3645962

Jaehyun Yi;Wook Joon Chung;Jeongwon Lee;Hamza Muzammal;Jeonghun Park;Young Soo Park;Yong-Lae Park

{"title":"A Multifingered Robotic Hand With Fiber-Optic Force and Tactile Sensing for Remote Manipulation","authors":"Jaehyun Yi;Wook Joon Chung;Jeongwon Lee;Hamza Muzammal;Jeonghun Park;Young Soo Park;Yong-Lae Park","doi":"10.1109/TRO.2025.3645962","DOIUrl":"10.1109/TRO.2025.3645962","url":null,"abstract":"Underactuated robotic hands are extensively used in remote manipulation due to their ability to adapt to various object sizes and shapes. Their structural simplicity and small number of actuators required for operation make them highly versatile and responsive, which is crucial for effective teleoperation. In addition to grasping performance, haptic feedback, which integrates force and tactile sensing, is essential for dexterous manipulation. This study proposes a solution using fiber-optic tendons embedded with fiber Bragg gratings (FBGs), combining sensing and actuation to simultaneously perform power transmission, along with force and tactile sensing. Each finger employs a fiber-optic tendon with three FBGs: one measures tendon tension, and the other two at the fingertip detect contact force and temperature. The tendon is placed on the volar side of the finger and routed to an actuation module with a servomotor at the wrist for power transmission. This tendon enables finger flexion, while a passive extension mechanism with linear springs on the dorsal side facilitates extension. Experimental results demonstrate the feasibility of this approach, showing the hand’s multifunctional capabilities, including haptic feedback and power transmission, as well as its potential for teleoperation. This approach improves the robotic hand’s ability to provide real-time feedback, improving dexterity in remote manipulation.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"509-524"},"PeriodicalIF":10.5,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Virtual Model Control for Robotics: Design and Tuning of Passivity-Based Controllers 机器人的最优虚拟模型控制：基于无源控制器的设计与整定

IF 10.5 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2025-12-18 DOI: 10.1109/TRO.2025.3645886

Daniel Larby;Fulvio Forni

引用次数: 0