IEEE Robotics and Automation Letters最新文献_第5页

A Model-Based Visual Contact Localization and Force Sensing System for Compliant Robotic Grippers 基于模型的柔性机械手视觉接触定位与力传感系统

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-11 DOI: 10.1109/LRA.2026.3692080

Kaiwen Zuo;Shuyuan Yang;Zonghe Chua

{"title":"A Model-Based Visual Contact Localization and Force Sensing System for Compliant Robotic Grippers","authors":"Kaiwen Zuo;Shuyuan Yang;Zonghe Chua","doi":"10.1109/LRA.2026.3692080","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692080","url":null,"abstract":"Grasp force estimation can help prevent robots from damaging delicate objects during manipulation and improve learning-based robotic control. Integrating force sensing into deformable grippers negotiates trade-offs in cost, complexity, mechanical robustness, and performance. With the growing integration of RGB-D wrist cameras into robotic systems for control purposes, camera-based techniques are a promising solution for indirect visual force estimation. Current approaches mostly utilize end-to-end deep learning, which can be brittle when generalizing to new scenarios, while existing model-based approaches are unsuited to grasping and modern grasper geometries. To address these challenges, we developed a model-based visual force sensing approach integrating an iterative contact localization with generalization to unseen objects. The system extracts structural key points from wrist camera RGB-D images of deforming fin-ray-shaped soft grippers, and uses these key points to define parameters of an inverse finite element analysis simulation in Simulation Open Framework Architecture. The iterative contact localization sub-system utilizes a deep learning-based online 3D reconstruction and pose estimation pipeline to dynamically update contact location, and is robust to visual occlusion and unseen objects. Our system demonstrated an average root mean square error of 0.23 N and normalized root mean square deviation of 2.11% during the load phase, and 0.48 N and 4.34% over the entire grasping process when interacting with different objects under various conditions, showcasing its potential for real-time model-based indirect force sensing of soft grippers.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7788-7795"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508927","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}

引用次数: 0

Multi-Session SLAM for Imaging Sonar Equipped Underwater Vehicles Using Semantic Scene Graphs 基于语义场景图的水下航行器成像声纳多时段SLAM

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-14 DOI: 10.1109/LRA.2026.3693583

John McConnell;Yewei Huang;Thomas Morris;Josh Doughty;Dennis Moynihan

{"title":"Multi-Session SLAM for Imaging Sonar Equipped Underwater Vehicles Using Semantic Scene Graphs","authors":"John McConnell;Yewei Huang;Thomas Morris;Josh Doughty;Dennis Moynihan","doi":"10.1109/LRA.2026.3693583","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693583","url":null,"abstract":"Accurate, reliable state estimation is an essential component in underwater autonomy. In underwater environments, simultaneous localization and mapping (SLAM) is often employed to provide a robot with state estimates, given the lack of GPS. SLAM state estimates exhibit error growth over time, which can be mitigated by re-observing a previously visited location (place recognition). However, this is trajectory-dependent, prone to outlier measurements, and, if a robot is tasked with driving to a place recognition destination, it could cost critical resources such as fuel or battery life. This motivates the multi-session SLAM problem, where a robot finds place recognition between its own data and a prior SLAM mission, enhancing the quality and robustness of the current SLAM solution. We propose a semantic scene graph method that enables robust multi-session SLAM. We detect objects in the environment, build scene graphs, and propose methods for place recognition and for estimating rigid-body transforms between prior and current scene graphs. By using semantic scene graphs, we show robustness to outliers and degeneracy in underwater sonar data. Additionally, the proposed method is compact, allowing it to be stored with minimal memory cost and transmitted to robots with low latency. We validate our method across three real-world environments, performing ablation studies on the proposed system and benchmarking against the literature. The views expressed in this document are those of the author(s) and do not reflect the official policy or position of the U.S. Naval Academy, Department of the Navy, the Department of War, or the U.S. Government.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8092-8099"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509047","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}

引用次数: 0

Dynamic Sphere Envelopes and SFF-DMP for Real-Time Obstacle Avoidance in Space Cable Assembly 空间电缆组件实时避障的动态球面包络和SFF-DMP

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-11 DOI: 10.1109/LRA.2026.3692057

Xumeng Cheng;Gangfeng Liu;Jie Zhao

{"title":"Dynamic Sphere Envelopes and SFF-DMP for Real-Time Obstacle Avoidance in Space Cable Assembly","authors":"Xumeng Cheng;Gangfeng Liu;Jie Zhao","doi":"10.1109/LRA.2026.3692057","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692057","url":null,"abstract":"This letter addresses real-time obstacle avoidance and precision manipulation in on-orbit cable disassembly tasks involving flexible, vibrating cables. We propose a control framework that combines dynamic sphere envelopes with a Steering Force Field enhanced Dynamic Movement Primitive (SFF-DMP) formulation. The dynamic sphere envelopes approximate oscillating cables with radius adaptive virtual obstacles, enabling compact geometric representations of flexible dynamics. The proposed SFF-DMP integrates a Cartesian-space DMP with a steering force field, enabling smooth and reliable obstacle avoidance with low free-space loss, while the joint-space DMP preserves demonstration similarity and tracking accuracy through null-space optimization. Cable dynamics modeling and simulation studies validate the method’s capability to avoid time-varying obstacles, and experiments on electrical connector disassembly demonstrate that a single cable free demonstration can generalize to multiple disturbed configurations. Results show consistent collision-free execution with position error below 0.7 mm and orientation error below 0.011 rd. The proposed approach offers a low-demonstration-cost, high-space-efficiency solution for safe manipulation in dynamic and constrained space environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7804-7811"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509612","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}

引用次数: 0

A Mid-Air Autonomous Battery Exchange Aerial System 一种空中自主电池交换空中系统

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-14 DOI: 10.1109/LRA.2026.3693549

Yooseung Choi;Jamie Henson;Kai Xue Keller;Clifford Gamble;Abhishek Kini;Ran Dai

引用次数: 0

Diffusion-Based Restoration for Multi-Modal 3D Object Detection in Adverse Weather 恶劣天气下多模态三维目标检测的扩散恢复方法

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-11 DOI: 10.1109/LRA.2026.3692085

Zhijian He;Feifei Liu;Yuwei Li;Zhanpeng Luo;Jintao Cheng;Xieyuanli Chen;Xiaoyu Tang

{"title":"Diffusion-Based Restoration for Multi-Modal 3D Object Detection in Adverse Weather","authors":"Zhijian He;Feifei Liu;Yuwei Li;Zhanpeng Luo;Jintao Cheng;Xieyuanli Chen;Xiaoyu Tang","doi":"10.1109/LRA.2026.3692085","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692085","url":null,"abstract":"Multi-modal 3D object detection is important for reliable perception in robotics and autonomous driving.However, its effectiveness remains limited under adverse weather conditions due to weather-induced distortions and misalignment between different data modalities. In this work, we propose DiffFusion, a novel framework designed to enhance robustness in challenging weather through diffusion-based restoration and adaptive cross-modal fusion. Our key insight is that diffusion models possess strong capabilities for denoising and generating data that can adapt to various weather conditions. Building on this, DiffFusion introduces Diffusion-IR restoring images degraded by weather effects and Point Cloud Restoration (PCR) compensating for corrupted LiDAR data using image object cues. To tackle misalignments between two modalities, we develop Bidirectional Adaptive Fusion and Alignment Module (BAFAM). It enables dynamic multi-modal fusion and bidirectional bird's-eye view (BEV) alignment to maintain consistent spatial correspondence. Extensive experiments on three public datasets show that DiffFusion achieves state-of-the-art robustness under adverse weather while preserving strong clean-data performance. Zero-shot results on the real-world DENSE dataset further validate its generalization.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8180-8187"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510746","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}

引用次数: 0

Side-Scan Sonar SLAM Using Ping-Level Landmark Detection in Feature-Poor Seabed Environments 在特征差的海底环境中使用平级地标检测的侧扫声纳SLAM

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-11 DOI: 10.1109/LRA.2026.3692094

Jinho Im;Seonghun Hong

{"title":"Side-Scan Sonar SLAM Using Ping-Level Landmark Detection in Feature-Poor Seabed Environments","authors":"Jinho Im;Seonghun Hong","doi":"10.1109/LRA.2026.3692094","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692094","url":null,"abstract":"Side-scan sonar (SSS) is a particularly attractive sensing modality for underwater simultaneous localization and mapping (SLAM), offering wide-area seabed coverage and reliable acoustic measurements over long ranges. Many existing SSS-based SLAM approaches rely on image-domain processing, leveraging acoustic images formed by stacking one-dimensional sonar pings. However, such approaches inherently depend on the availability of sufficiently rich image-domain features and can therefore struggle in feature-poor or homogeneous seabed environments where distinctive visual structures are limited. Furthermore, even when image-domain features are present, range-dependent intensity variations and speckle noise inherent in SSS measurements can degrade the reliability of feature extraction and data association. This study proposes a ping-level SSS SLAM framework that directly exploits raw backscatter intensity profiles without relying on image formation. By characterizing the nominal seafloor response and identifying structurally salient deviations in the acoustic intensity profiles, reliable landmark measurements are extracted at the ping level and incorporated into a landmark-based SLAM framework. This formulation preserves the native sensing geometry of SSS measurements and enables robust landmark extraction even in feature-sparse environments. The proposed approach is validated through both physics-based simulations and real-world field experiments, demonstrating improved robustness and localization accuracy in challenging seabed conditions.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7900-7907"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510758","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}

引用次数: 0

PEMTRS: Perception-Enhanced Memory With Temporal Region Selection for Vision-Based Multirotor Navigation 基于视觉的多旋翼导航的颞区选择感知增强记忆

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-12 DOI: 10.1109/LRA.2026.3692098

Chenfeng Guo;Chen Su;Zhaopeng Zhang;Jianda Han;Yongchun Fang;Xiao Liang

{"title":"PEMTRS: Perception-Enhanced Memory With Temporal Region Selection for Vision-Based Multirotor Navigation","authors":"Chenfeng Guo;Chen Su;Zhaopeng Zhang;Jianda Han;Yongchun Fang;Xiao Liang","doi":"10.1109/LRA.2026.3692098","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692098","url":null,"abstract":"End-to-end deep learning methods for autonomous aerial vehicles like multirotors have been successfully applied to high-agility flight. However, existing end-to-end navigation methods for multirotors typically rely solely on the robot's current observation and state, neglecting explicit modeling of temporal sequence information. Yet trajectory prediction is inherently temporal: past observations provide cues about future feasible regions, and this temporal feasibility is still not explicitly exploited in current end-to-end navigation frameworks. To overcome these limitations, we propose the PEMTRS framework, which enables the multirotor to fly with thinking by building a perception-enhanced memory structure and employing a temporal region selector to highlight regions of interest for traversability. By jointly modeling spatial perception and temporal information, PEMTRS generalizes better within the evaluated environment class than state-of-the-art end-to-end methods. To achieve more efficient training, we adopt an implicit learning strategy that replaces expert-labeled demonstration trajectories with cost-based learning signals to guide network optimization. In addition, we design an adaptive time-allocation network to dynamically adjust the time intervals between waypoints and a residual VAE to reconstruct obstacle depth for perception enhancement. Finally, simulation and real-world experiments validate the effectiveness and efficiency of the proposed method.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7884-7891"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510760","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}

引用次数: 0

LOPAL: Local Performance-Aware Active Learning From Imperfect Demonstrations 从不完美的演示中进行局部性能感知的主动学习

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-29 DOI: 10.1109/LRA.2026.3698364

Johannes Heidersberger;Shail Jadav;Dongheui Lee

{"title":"LOPAL: Local Performance-Aware Active Learning From Imperfect Demonstrations","authors":"Johannes Heidersberger;Shail Jadav;Dongheui Lee","doi":"10.1109/LRA.2026.3698364","DOIUrl":"https://doi.org/10.1109/LRA.2026.3698364","url":null,"abstract":"Learning from Demonstration (LfD) enables intuitive robot skill acquisition by allowing robots to learn directly from human task demonstrations. However, current methods often fail to address the fact that due to suboptimal and inconsistent human behavior, the quality of the demonstration can vary within each demonstration. Therefore, we introduce LOPAL (LOcal Performance-aware Active Learning), an active learning approach that leverages this local demonstration quality information. Our approach consists of two synergistic components. First, a local performance-driven LfD method uses a Gaussian Mixture Model (GMM) to encode both the demonstrated trajectories and their associated local quality assessments. This enables the generation of trajectories that outperform the imperfect demonstrations by utilizing complementary local data of high performance. Second, active data acquisition allows to improve beyond the imperfect demonstrations by collecting additional informative samples. In areas missing good data, the user is actively requested to provide corrections through a shared autonomy (SA) mechanism, while the robot autonomously executes the learned behavior. The efficacy of LOPAL was validated in both a simulation and a real-world experiment. The results from a real-world pipe inspection task showed that the proposed approach can achieve up to 27.31% improvement in task performance while also reducing the effort required to collect the demonstrations.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"9040-9047"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11538996","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442445","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}

引用次数: 0

IEEE Robotics and Automation Society Information IEEE机器人与自动化学会信息

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-26 DOI: 10.1109/LRA.2026.3705164

引用次数: 0

H-VSJ: A Hybrid Stiff-Compliant Variable Stiffness Joint With Decoupling Position and Stiffness Modulation 一种位置解耦和刚度调制的刚柔变刚度混合关节

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/LRA.2026.3701570

Lei Yan;Junpeng Gu;Ziye Zheng;Sheng Liu;Shilong Sun;Fujun Peng;Wenfu Xu

{"title":"H-VSJ: A Hybrid Stiff-Compliant Variable Stiffness Joint With Decoupling Position and Stiffness Modulation","authors":"Lei Yan;Junpeng Gu;Ziye Zheng;Sheng Liu;Shilong Sun;Fujun Peng;Wenfu Xu","doi":"10.1109/LRA.2026.3701570","DOIUrl":"https://doi.org/10.1109/LRA.2026.3701570","url":null,"abstract":"The wide spectrum of robotic applications motivates the intrinsic stiffness modulation of the robot besides compliance control, especially for highly dynamic manipulation. Variable Stiffness Actuators (VSAs) are designed for improving impact resistance, stiffness adaptability, and dynamic performance, while many existing variable stiffness mechanisms still face coupling between position and stiffness modulation. This letter presents a Hybrid Stiff-Compliant Variable Stiffness Joint (H-VSJ) that employs a decoupling mechanism to achieve independent control of joint position and stiffness as well as hybrid stiff-compliant modes. By adjusting the force-transmission ratio between the elastic element and the output link along the joint axis, H-VSJ achieves the decoupled and accurate stiffness modulation. The decoupling mechanism also allows the joint to smoothly switch between the stiff mode and compliant mode. The stiffness model of H-VSJ is established, and the influence of key design parameters on stiffness is further analyzed. Finally, the effectiveness of the proposed H-VSJ is validated through experiments on buffering motion range, stiffness-position decoupling, stiffness analysis, dynamic stiffness modulation, and impact-buffering capability, which demonstrate H-VSJ has a maximum deflection of <inline-formula><tex-math>$30.20^circ$</tex-math></inline-formula> in the low-stiffness state, an average stiffness modulation error of <inline-formula><tex-math>$7.00$</tex-math></inline-formula>%, a stiffness range of 23.57–260.36 N m/rad in the compliant mode, and 8630.25 N m/rad in the stiff mode.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8944-8951"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443046","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}

引用次数: 0