IEEE Robotics and Automation Letters最新文献_第2页

Corrections to “Following to Talk: Effects of Mobile Robot Guidance on Dialogue Motivation in a Field Trial” 对“跟随谈话：移动机器人引导对现场试验中对话动机的影响”的更正

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.2026.3697952

Masaya Iwasaki;Kazuki Masuda;Kazuki Sakai;Midori Ban;Zihao Chi;Megumi Kawata;Alexis Meneses;Hiroshi Ishiguro;Yuichiro Yoshikawa

引用次数: 0

Real-to-Sim for Highly Cluttered Environments via Physics-Consistent Inter-Object Reasoning 通过物理一致的对象间推理实现高度混乱环境的真实模拟

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-01 DOI: 10.1109/LRA.2026.3699238

Tianyi Xiang;Jiahang Cao;Sikai Guo;Guoyang Zhao;Andrew F. Luo;Jun Ma

{"title":"Real-to-Sim for Highly Cluttered Environments via Physics-Consistent Inter-Object Reasoning","authors":"Tianyi Xiang;Jiahang Cao;Sikai Guo;Guoyang Zhao;Andrew F. Luo;Jun Ma","doi":"10.1109/LRA.2026.3699238","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699238","url":null,"abstract":"Reconstructing physically valid 3D scenes from single-view observations is a prerequisite for bridging the gap between visual perception and robotic control. However, in scenarios requiring precise contact reasoning, such as robotic manipulation in highly cluttered environments, geometric fidelity alone is insufficient. Standard perception pipelines often neglect physical constraints, resulting in invalid states, e.g., floating objects or severe inter-penetration, rendering downstream simulation unreliable. To address these limitations, we propose a novel physics-constrained Real-to-Sim pipeline that reconstructs physically consistent 3D scenes from single-view RGB-D data. Central to our approach is a differentiable optimization pipeline that explicitly models spatial dependencies via a contact graph, jointly refining object poses and physical properties through differentiable rigid-body simulation. Extensive evaluations in both simulation and real-world settings demonstrate that our reconstructed scenes achieve high physical fidelity and faithfully replicate real-world contact dynamics, enabling stable and reliable contact-rich manipulation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8544-8551"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627691","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

Lie-Algebraic Approach to Geometric Nonlinear $mathcal {H}_infty$ Inverse Optimal Attitude Tracking on $SO(3)$ 几何非线性的lie -代数方法$mathcal {H}_infty$逆最优姿态跟踪 $SO(3)$

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-04 DOI: 10.1109/LRA.2026.3700377

Junsik Kim;Youngjun Joo;Youngjin Choi

{"title":"Lie-Algebraic Approach to Geometric Nonlinear $mathcal {H}_infty$ Inverse Optimal Attitude Tracking on $SO(3)$","authors":"Junsik Kim;Youngjun Joo;Youngjin Choi","doi":"10.1109/LRA.2026.3700377","DOIUrl":"https://doi.org/10.1109/LRA.2026.3700377","url":null,"abstract":"This letter presents a geometric nonlinear <inline-formula><tex-math>$mathcal {H}_infty$</tex-math></inline-formula> inverse optimal attitude tracking framework formulated on <inline-formula><tex-math>$SO(3)$</tex-math></inline-formula> using its Lie algebra <inline-formula><tex-math>$mathfrak {so}(3)$</tex-math></inline-formula>. By adopting exponential coordinates <inline-formula><tex-math>$bm {xi }in mathfrak {so}(3)$</tex-math></inline-formula>, attitude errors are mapped directly onto the Lie algebra. This preserves a bijective, distortion-free relationship with the geodesic distance within the injectivity radius (<inline-formula><tex-math>$Vert bm {xi }Vert < pi$</tex-math></inline-formula>), while eliminating singularities and unwinding issues inherent in Euler angles and quaternions. A sliding-mode-inspired nonlinear <inline-formula><tex-math>$mathcal {H}_infty$</tex-math></inline-formula> controller is developed, with robust stability established using the exponential coordinates and a systematic tuning methodology provided to facilitate practical implementation. Quadrotor simulations demonstrate faster convergence in large-angle maneuvers by mitigating the vanishing error and sluggish response of conventional methods. Experiments on a robotic manipulator validate the tuning rule for the prescribed disturbance attenuation level <inline-formula><tex-math>$gamma$</tex-math></inline-formula>, showing that the peak tracking error scales as <inline-formula><tex-math>$mathcal {O}(gamma ^{2})$</tex-math></inline-formula> for small gains and <inline-formula><tex-math>$mathcal {O}(gamma)$</tex-math></inline-formula> for large gains. These results demonstrate the robustness and predictability of the proposed framework and the effectiveness of Lie algebra-based attitude control on <inline-formula><tex-math>$SO(3)$</tex-math></inline-formula>.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8920-8927"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442265","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

CPP: Cooperative Priority Planning for Multi-Agent Path Finding With Coordination and Action Durations 具有协调和行动持续时间的多智能体寻径的合作优先级规划

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-15 DOI: 10.1109/LRA.2026.3693986

Zhengchen Li;Boyu Li;Weimin Wu;Dacheng Li

{"title":"CPP: Cooperative Priority Planning for Multi-Agent Path Finding With Coordination and Action Durations","authors":"Zhengchen Li;Boyu Li;Weimin Wu;Dacheng Li","doi":"10.1109/LRA.2026.3693986","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693986","url":null,"abstract":"Modern multi-agent systems are evolving toward scalability, heterogeneity, and collaboration, where agents are required to coordinate to perform shared tasks. However, existing Cooperative Multi-Agent Path Finding (Co-MAPF) research typically assumes instantaneous actions. In reality, individual and joint actions require non-negligible durations, which introduce additional state dimensions that significantly expand the conflict-resolution search space. This letter formulates the problem as Co-MAPF with Action Durations and proposes Cooperative Priority Planning (CPP). CPP maintains high-level constraints and a priority queue to serialize task allocation and optimize meeting vertex selection, while employing a low-level solver that integrates synchronization windows with Time-Constrained A* to couple safe action durations with conflict-free path planning. Experimental results demonstrate that CPP achieves near-linear empirical time complexity and superior scalability. It exhibits near-optimal path quality with a small optimality gap in small-scale scenarios, while scaling more effectively than existing Co-MAPF solvers to constrained scenarios. Moreover, it maintains high success rates in large-scale, constrained scenarios, and ablation studies validate the effectiveness of each component.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8976-8983"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442951","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

Force Estimation With Concentric-Tube Robots for Surgical Palpation 同心管机器人在外科触诊中的力估计

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.2026.3701528

Yufei Wu;Joshua Gaston;Samuel Tobin;Lauren Branscombe;Caleb Rucker

{"title":"Force Estimation With Concentric-Tube Robots for Surgical Palpation","authors":"Yufei Wu;Joshua Gaston;Samuel Tobin;Lauren Branscombe;Caleb Rucker","doi":"10.1109/LRA.2026.3701528","DOIUrl":"https://doi.org/10.1109/LRA.2026.3701528","url":null,"abstract":"This paper presents the design, modeling, and experimental verification of a deflection-based force-sensing probe in a concentric-tube robot for palpation during robotic surgery. The proposed method leverages the inherent elastic compliance of the robot and a quasi-static Cosserat rod model to estimate contact forces from real-time tip deflections measured by a 5-DOF magnetic tracker, eliminating the need for distal force sensors. The system was experimentally validated on the Virtuoso Endoscopy System (Virtuoso Surgical, Nashville, TN) across multiple palpation locations and directions, including experiments on compliant tissue phantoms. The proposed method achieved a mean absolute force error of approximately 0.05 N, evaluated using a leave-one-group-out cross-validation scheme across palpation locations. Simulation studies using a real-time deformable-tissue model further demonstrate the tool’s potential for automated tissue stiffness mapping, which is further demonstrated by stiffness mapping a tissue phantom to localize an embedded simulated tumor. These results establish a compact, sensor-efficient, and accurate framework for model-based force sensing in continuum robotic palpation, providing a foundation for future clinical applications in minimally invasive surgery.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8896-8903"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442983","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

Lighting-Robust Behavioral Cloning via Bayesian Visual Encoding for Robot Manipulation 基于贝叶斯视觉编码的机器人操作光照鲁棒行为克隆

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.2026.3692063

Zhenyuan Dong;Dongxu Han;Zhi Wang;Zhiyi Yan;Qian Lin;Ziyi Zhang;Peng Zhang

{"title":"Lighting-Robust Behavioral Cloning via Bayesian Visual Encoding for Robot Manipulation","authors":"Zhenyuan Dong;Dongxu Han;Zhi Wang;Zhiyi Yan;Qian Lin;Ziyi Zhang;Peng Zhang","doi":"10.1109/LRA.2026.3692063","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692063","url":null,"abstract":"Behavioral cloning (BC) has demonstrated strong performance in robot manipulation by learning visuomotor policies directly from expert demonstrations. However, under illumination-induced distribution shifts, vision-based BC policies often become sensitive to spurious visual variations, leading to degraded reliability and generalization. To address this issue, we propose a lighting-robust BC framework that incorporates Bayesian deep learning into the visual encoder. Specifically, we model a posterior distribution over convolutional weights and optimize the evidence lower bound, where a Kullback–Leibler regularization term constrains feature learning under illumination variability. In simulation, the proposed method achieves higher success rates and shorter rollout lengths than deterministic baselines, while maintaining strong sample efficiency with a single Monte Carlo sample. In real-world experiments across seven illumination regimes, including distribution-shifted conditions, our method consistently outperforms BC baselines and remains stable under severe lighting changes. These results indicate that Bayesian regularization provides an effective inductive bias for learning visual representations that improve robustness and generalization in vision-based robot manipulation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8236-8243"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504459","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

TGS-SLAM: Tri-Plane Gaussian Splatting for Semantic SLAM TGS-SLAM：语义SLAM的三平面高斯溅射

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.2026.3692078

Guoxi Sun;Handong Shen;Xiaohao Liu;Xinchao Li;Lingyu Liang;Beibei Liu;Shuangping Huang

{"title":"TGS-SLAM: Tri-Plane Gaussian Splatting for Semantic SLAM","authors":"Guoxi Sun;Handong Shen;Xiaohao Liu;Xinchao Li;Lingyu Liang;Beibei Liu;Shuangping Huang","doi":"10.1109/LRA.2026.3692078","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692078","url":null,"abstract":"Semantic SLAM aims to build 3D maps that are both geometrically accurate and semantically consistent for downstream tasks such as navigation, manipulation, and AR/VR. Existing dense neural approaches largely follow two paradigms: implicit neural fields, which offer strong cross-view consistency but rely on expensive NeRF-style volumetric rendering, and 3D Gaussian Splatting (3DGS), which is efficient but typically attaches semantics to individual Gaussians, yielding fragmented primitive-level semantics and semantic parameters that scale linearly with the number of primitives. We present TGS-SLAM, a semantic SLAM system that anchors geometry and appearance with 3D Gaussians while encoding semantics in TriDS, a shared coarse-to-fine tri-plane field decoupled from Gaussian primitives. Semantic features are queried at Gaussian centers, decoded into class logits, and splatted jointly with color and depth in a single rendering pass. We further adopt a geometry-first optimization schedule to stabilize the Gaussian map before semantic learning, and a hybrid pose initialization mechanism for robust tracking. Experiments on Replica, ScanNet, and ScanNet++ demonstrate that TGS-SLAM produces coherent semantic maps with competitive tracking and reconstruction quality. On Replica, it achieves <bold>97.0%</b> mIoU while reducing semantic parameters by <bold><inline-formula><tex-math>$sim 100times$</tex-math></inline-formula></b> compared with per-Gaussian one-hot encodings.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8300-8307"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504466","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

Disturbance-Robust Dynamical System Learning With Neural ODEs and Flow-Matching Augmentation 扰动-鲁棒动态系统学习与神经ode和流匹配增强

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.2026.3692049

Bang Liu;Pingyun Nie;Zhuang Fu;Tianxiang Jiang;Zi Fang;Jianfeng Yao;Letian Li

{"title":"Disturbance-Robust Dynamical System Learning With Neural ODEs and Flow-Matching Augmentation","authors":"Bang Liu;Pingyun Nie;Zhuang Fu;Tianxiang Jiang;Zi Fang;Jianfeng Yao;Letian Li","doi":"10.1109/LRA.2026.3692049","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692049","url":null,"abstract":"Autonomous dynamical systems (DS) are essential for imitation learning but often face challenges in simultaneously achieving high accuracy, stability guarantees, and resistance to disturbances. To overcome these limitations, this paper proposes a globally stable DS with trajectory attraction and disturbance robustness, termed K-Canyon DS (KCDS). The method first employs a Neural ODE to learn a diffeomorphic mapping from the task space to a linear latent space, thereby enhancing learning accuracy while ensuring global stability. Subsequently, based on clustering of starting points in the linear latent space, a nonlinear learnable trajectory-attractive field with a canyon-like structure is introduced to confer disturbance robustness. System parameters are trained via flow matching-based data augmentation, ultimately achieving a globally stable and disturbance-robust DS design. The proposed method is thoroughly evaluated on 2D and 3D LASA datasets, including comparative experiments with open-source baselines under disturbance. Real-robot experiments further demonstrate its practical applicability.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7756-7763"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504467","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

Design and Control of Centimeter-Scale Reconfigurable Aquatic Modular Robots 厘米级可重构水生模块化机器人的设计与控制

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-15 DOI: 10.1109/LRA.2026.3693985

Kevin Macauley;Michael Zhao;Zhiheng Chen;Wei Wang

引用次数: 0

Tactile Memory With Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist 软机器人的触觉记忆：基于掩码编码和软手腕的鲁棒对象插入

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.2026.3692097

Tatsuya Kamijo;Mai Nishimura;Nodoka Shibasaki;Jeremy Siburian;Cristian C. Beltran-Hernandez;Masashi Hamaya

{"title":"Tactile Memory With Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist","authors":"Tatsuya Kamijo;Mai Nishimura;Nodoka Shibasaki;Jeremy Siburian;Cristian C. Beltran-Hernandez;Masashi Hamaya","doi":"10.1109/LRA.2026.3692097","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692097","url":null,"abstract":"Tactile memory, the ability to store and retrieve touch-based experience, is critical for contact-rich tasks such as key insertion under uncertainty. To replicate this capability, we introduce Tactile Memory with Soft Robot (TaMeSo-bot), a system that integrates a soft wrist with tactile retrieval-based control to enable safe and robust manipulation. The soft wrist allows safe contact exploration during data collection, while tactile memory reuses past demonstrations via retrieval for flexible adaptation to unseen scenarios. The core of this system is the Masked Tactile Trajectory Transformer (MAT<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>), which jointly models spatiotemporal interactions between robot actions, distributed tactile cues, force-torque measurements, and proprioceptive signals. Through masked token prediction, MAT<inline-formula><tex-math>$^{3}$</tex-math></inline-formula> learns rich spatiotemporal representations by inferring missing sensory information from context, autonomously extracting task-relevant features without explicit subtask segmentation. We validate our approach on peg-in-hole tasks with diverse pegs and conditions in real-robot experiments. Our extensive evaluation demonstrates that MAT<inline-formula><tex-math>$^{3}$</tex-math></inline-formula> achieves higher success rates than the baselines over all conditions and shows remarkable capability to adapt to unseen pegs and conditions.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7844-7851"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508929","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