IEEE Robotics and Automation Letters最新文献

IEEE Robotics and Automation Letters Information for Authors IEEE机器人与自动化作者信函信息

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-08-01 Epub Date: 2026-07-23 DOI: 10.1109/LRA.2026.3712845

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 : 2026-08-01 Epub Date: 2026-07-23 DOI: 10.1109/LRA.2026.3712843

引用次数: 0

IEEE Robotics and Automation Society Publication Information IEEE机器人与自动化协会出版信息

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-08-01 Epub Date: 2026-07-23 DOI: 10.1109/LRA.2026.3712841

引用次数: 0

Open-Vocabulary Semantic Segmentation for Dynamic 3D Scenes Using Scene Flow Estimation 基于场景流估计的动态3D场景开放词汇语义分割

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.2026.3693993

You-Jun Li;Yu-Kai Lin;Bang-Shien Chen;Chih-Wei Huang;Jann-Long Chern;Ching-Cherng Sun

{"title":"Open-Vocabulary Semantic Segmentation for Dynamic 3D Scenes Using Scene Flow Estimation","authors":"You-Jun Li;Yu-Kai Lin;Bang-Shien Chen;Chih-Wei Huang;Jann-Long Chern;Ching-Cherng Sun","doi":"10.1109/LRA.2026.3693993","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693993","url":null,"abstract":"3D open-vocabulary semantic segmentation has shown great potential in applications such as autonomous driving and mixed reality. However, achieving accurate segmentation in dynamic environments remains challenging due to motion-induced inconsistencies. To address this issue, we incorporate scene flow as temporal information into a static semantic backbone to enhance semantic consistency and accuracy over time. Our method captures inter-frame motion cues from point cloud sequences and leverages them, together with a local clustering mechanism, to refine semantic label consistency in consecutive frames. Furthermore, we introduce a two-way scene flow-based data augmentation strategy that exploits both forward and backward motion to jointly train the model in bidirectional temporal contexts. On the large-scale nuScenes autonomous driving dataset, our method achieves a 0.4% overall improvement in hIoU and a 2.37% gain under high-motion scenes. On the synthetic object-centric dataset, it achieves a 4.53% overall hIoU improvement and a 6.09% gain in high-motion scenes, while reducing the ID switch rate by 0.5%.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8140-8147"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504507","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

Certifiable Alignment of GNSS and Local Frames via Lagrangian Duality 基于拉格朗日对偶的GNSS和局部帧的可认证对准

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.2026.3693943

Baoshan Song;Matthew Giamou;Penggao Yan;Chunxi Xia;Li-Ta Hsu

{"title":"Certifiable Alignment of GNSS and Local Frames via Lagrangian Duality","authors":"Baoshan Song;Matthew Giamou;Penggao Yan;Chunxi Xia;Li-Ta Hsu","doi":"10.1109/LRA.2026.3693943","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693943","url":null,"abstract":"Estimating the absolute orientation of a local system relative to a global navigation satellite system (GNSS) reference often suffers from local minima and high dependency on satellite availability. Existing methods for this alignment task rely on abundant satellites unavailable in GNSS-degraded environments, or use local optimization methods which cannot guarantee the optimality of a solution. This work proposed a certifiable method, meaning it can numerically verify the optimality of the result, filling a gap where existing local optimizers fail. We first formulate the original Doppler-based frame alignment problem as a nonconvex quadratically constrained quadratic program (QCQP) problem and relax the QCQP problem to a concave Lagrangian dual problem that provides a lower cost bound for the original problem. Then we perform relaxation tightness and observability analysis to derive criteria for certifiable optimality of the solution. Finally, simulation and real world experiments are conducted to evaluate the proposed method. The experiments show that our method provides certifiably optimal solutions even with only 2 satellites with Doppler measurements and 2D vehicle motion, while the traditional velocity-based VOBA method and the advanced GVINS alignment technique may fail or converge to local optima without notice.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8196-8203"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508796","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

Three Percent is Enough: Semi-Supervised Martian Segmentation Labeling With Active Learning 百分之三是足够的：半监督火星分割标记与主动学习

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-14 DOI: 10.1109/LRA.2026.3693576

Muyao Li;Ruyi Zhou;Liang Ding;Huaiguang Yang;Haibo Gao;Zongquan Deng

{"title":"Three Percent is Enough: Semi-Supervised Martian Segmentation Labeling With Active Learning","authors":"Muyao Li;Ruyi Zhou;Liang Ding;Huaiguang Yang;Haibo Gao;Zongquan Deng","doi":"10.1109/LRA.2026.3693576","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693576","url":null,"abstract":"Accurate, large-scale Martian segmentation datasets are a cornerstone of autonomous scene understanding in support of exploration and navigation in Martian environments. However, high-quality segmentation labeling on planetary images requires annotators to have professional extraterrestrial geological knowledge, and even skilled annotators need a long time to label a single image in detail. In this paper, we propose a semi-automatic annotation method for Martian scene segmentation. By integrating a semi-supervised segmentation network architecture with an active learning strategy, our framework achieves near-fully supervised performance using a minimal amount of manual annotations, significantly reducing dependence on human experts. Experiments on the S<inline-formula><tex-math>$^{5}$</tex-math></inline-formula>Mars dataset show that our framework reaches 77.01% mIoU with only 3.13% of the manual annotations (169 images), corresponding to 92.23% of the performance (83.50% mIoU) of the official fully supervised model trained on 5400 labeled images. We also conducted an extended experiment on AI4Mars, where the proposed framework consistently achieved strong performance, exceeding the fully supervised baseline by 8.12% using only 3.12% of labeled data. This annotation ratio is substantially lower than the nearly 20% labeled data typically required by conventional semi-supervised approaches, highlighting the efficiency of our method for large-scale Martian scene annotation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8156-8163"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508923","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 Policy Learning for Legged Robot With Simplified Model Pretraining and Model-Homotopy-Inspired Transfer 基于简化模型预训练和模型同伦启发迁移的腿式机器人动态策略学习

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-11 DOI: 10.1109/LRA.2026.3692082

Dongyun Kang;Min-Gyu Kim;Tae-Gyu Song;Hajun Kim;Sehoon Ha;Hae-Won Park

{"title":"Dynamic Policy Learning for Legged Robot With Simplified Model Pretraining and Model-Homotopy-Inspired Transfer","authors":"Dongyun Kang;Min-Gyu Kim;Tae-Gyu Song;Hajun Kim;Sehoon Ha;Hae-Won Park","doi":"10.1109/LRA.2026.3692082","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692082","url":null,"abstract":"Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various legged locomotion tasks, producing highly dynamic behaviors often requires extensive reward tuning or high-quality demonstrations. Leveraging reduced-order models can help mitigate these challenges. However, the model discrepancy poses a significant challenge when transferring policies to full-body dynamics environments. In this work, we introduce a continuation-based learning framework that combines simplified model pretraining and model-homotopy-inspired transfer to efficiently generate and refine complex dynamic behaviors. First, we pretrain the policy using a single rigid body model to capture core motion patterns in a simplified environment. Next, we employ a continuation strategy to progressively transfer the policy to the full-body environment, minimizing performance loss. To define the continuation path, we introduce a parametric transition path from the single rigid body model to the full-body model by gradually redistributing mass and inertia between the trunk and legs. The proposed method achieves faster convergence and demonstrates superior stability during the transfer process compared to baseline methods. Our framework is validated on a range of dynamic tasks, including flips and wall-assisted maneuvers, and is successfully deployed on a real quadrupedal robot.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8068-8075"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509049","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

Equivariant Filter for Radar-Inertial Odometry 雷达-惯性测程的等变滤波器

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.2026.3692026

Giulio Delama;Jan Michalczyk;Morten Nissov;Martin Scheiber;Alessandro Fornasier;Kostas Alexis;Stephan Weiss

{"title":"Equivariant Filter for Radar-Inertial Odometry","authors":"Giulio Delama;Jan Michalczyk;Morten Nissov;Martin Scheiber;Alessandro Fornasier;Kostas Alexis;Stephan Weiss","doi":"10.1109/LRA.2026.3692026","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692026","url":null,"abstract":"Radar-Inertial Odometry (RIO) based on the Extended Kalman Filter (EKF) relies on accurate extrinsic calibration between the radar and the Inertial Measurement Unit (IMU) and is sensitive to disturbances, as large linearization errors can degrade performance or even cause divergence. To address these limitations, this letter proposes an Equivariant Filter (EqF) for RIO based on a Lie group symmetry that geometrically couples navigation states and IMU biases, extending it to incorporate radar-IMU extrinsic calibration and multi-state constraint updates. This equivariant formulation inherently preserves consistency and enhances robustness, enabling reliable state estimation even under poor or completely wrong initialization of calibration states. Real-world experiments on two different Uncrewed Aerial Vehicles (UAVs) show that the proposed EqF-RIO achieves state-of-the-art accuracy under correct extrinsic calibration and offers improved convergence under large calibration errors, where the conventional EKF-RIO fails. Evaluation code is open-sourced.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7812-7819"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11513737","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509614","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

RLRC: Reinforcement Learning-Based Recovery for Compressed Vision-Language-Action Models RLRC：基于强化学习的压缩视觉-语言-动作模型恢复

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.2026.3700379

Yuxuan Chen;Yixin Han;Yize Huang;Xiao Li

引用次数: 0

A Novel Method With Encoder-Decoder for Cross-Sensor Adaptation in Surface Shape Sensing With Sparse Strain Sensors 一种基于编码器-解码器的稀疏应变传感器表面形状感知跨传感器自适应方法

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-01 DOI: 10.1109/LRA.2026.3699130

Shuo Wang;Heng Luo;Dian Jin;Xiaoming Tao

{"title":"A Novel Method With Encoder-Decoder for Cross-Sensor Adaptation in Surface Shape Sensing With Sparse Strain Sensors","authors":"Shuo Wang;Heng Luo;Dian Jin;Xiaoming Tao","doi":"10.1109/LRA.2026.3699130","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699130","url":null,"abstract":"Performance variations in sensor arrays, caused by intrinsic differences or installation conditions, can lead to inconsistent results during shape sensing. To obtain accurate results, a large amount of data is usually required, and a separate model must be retrained for each sensor array, thereby increasing the cost and time of data acquisition, transmission, and computation. To address this issue, this work proposes an encoder-decoder architecture for surface shape sensing based on sparse strain sensors and further incorporates meta-learning and few-shot adaptation strategies to enable adaptation across different groups of sensor arrays. Experimental results demonstrate that, after the cross-sensor adaptation, a newly deployed sensor array achieves a sensing error of approximately 4.0 mm relying on less than 5.0% newly labeled data and requiring an adaptation time of under 1 s, which represents a substantial improvement from 23.0 mm error without adaptation and 20-minute data collection time required to train a new model. Moreover, the number of points with errors below 5.0 mm increased by more than 65.0%. These results indicate that the proposed method can substantially reduce the cost and training burden of surface shape sensing, and it has broad potential applications in soft robotics and wearable devices.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8600-8607"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627428","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