IEEE Robotics and Automation Letters最新文献

IEEE Robotics and Automation Letters Information for Authors IEEE 《机器人与自动化通讯》作者须知

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-23 DOI: 10.1109/LRA.2025.3561983

引用次数: 0

IEEE Robotics and Automation Society Information 电气和电子工程师学会机器人与自动化协会信息

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-23 DOI: 10.1109/LRA.2025.3561981

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 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-23 DOI: 10.1109/LRA.2025.3561979

引用次数: 0

Integrating Reinforcement Learning and Virtual Fixtures for Safer Automatic Robotic Surgery 集成强化学习和虚拟夹具的更安全自动机器人手术

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-15 DOI: 10.1109/LRA.2025.3559826

Ke Fan;Ziyang Chen

{"title":"Integrating Reinforcement Learning and Virtual Fixtures for Safer Automatic Robotic Surgery","authors":"Ke Fan;Ziyang Chen","doi":"10.1109/LRA.2025.3559826","DOIUrl":"https://doi.org/10.1109/LRA.2025.3559826","url":null,"abstract":"A primary concern in robotic automation is safety, especially in surgical scenarios. In this letter, we propose a virtual fixture (VF) based safe reinforcement learning framework to ensure safety constraints. The framework ensures that the agent, particularly multi-joint robotic manipulator agents, acts within the hard constraints. In the training phase, VF confines the exploration of the agent within a safe operational space. The core idea is that once the agent violates the VF, it will be pushed back to the safe region. Then, the safe action corrected by the VF is collected and forms a safe experience used for subsequent policy optimization, which we refer to as safety experience reshaping (SER). Subsequently, we design a visual module to detect safety constraints to construct the VF and transfer the trained policy to the real robot. We compare our framework to 5 state-of-the-art RL methods and a nonlearning-based method. Results show that our framework gets a lower rate of constraint violations and better performance in task success. Furthermore, in addition to the static constraint tasks, we also designed two tasks involving dynamic constraints, highlighting the superiority of our method in handling dynamic constraints. The videos of our physical experiment can be found in the following links (Lymph node removal, Human-robot collaboration 1, Human-robot collaboration 2).","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5265-5272"},"PeriodicalIF":4.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848902","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

Learning Cross-Modal Visuomotor Policies for Autonomous Drone Navigation 自主无人机导航的跨模态视觉运动策略学习

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-10 DOI: 10.1109/LRA.2025.3559824

Yuhang Zhang;Jiaping Xiao;Mir Feroskhan

{"title":"Learning Cross-Modal Visuomotor Policies for Autonomous Drone Navigation","authors":"Yuhang Zhang;Jiaping Xiao;Mir Feroskhan","doi":"10.1109/LRA.2025.3559824","DOIUrl":"https://doi.org/10.1109/LRA.2025.3559824","url":null,"abstract":"Developing effective vision-based navigation algorithms adapting to various scenarios is a significant challenge for autonomous drone systems, with vast potential in diverse real-world applications. This paper proposes a novel visuomotor policy learning framework for monocular autonomous navigation, combining cross-modal contrastive learning with deep reinforcement learning (DRL) to train a visuomotor policy. Our approach first leverages contrastive learning to extract consistent, task-focused visual representations from high-dimensional RGB images as depth images, and then directly maps these representations to action commands with DRL. This framework enables RGB images to capture structural and spatial information similar to depth images, which remains largely invariant under changes in lighting and texture, thereby maintaining robustness across various environments. We evaluate our approach through simulated and physical experiments, showing that our visuomotor policy outperforms baseline methods in both effectiveness and resilience to unseen visual disturbances. Our findings suggest that the key to enhancing transferability in monocular RGB-based navigation lies in achieving consistent, well-aligned visual representations across scenarios, which is an aspect often lacking in traditional end-to-end approaches.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5425-5432"},"PeriodicalIF":4.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860761","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

RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement 基于rgb的基于深度恢复和自适应细化的类别级目标姿态估计

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-10 DOI: 10.1109/LRA.2025.3559841

Hui Yang;Wei Sun;Jian Liu;Jin Zheng;Zhiwen Zeng;Ajmal Mian

{"title":"RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement","authors":"Hui Yang;Wei Sun;Jian Liu;Jin Zheng;Zhiwen Zeng;Ajmal Mian","doi":"10.1109/LRA.2025.3559841","DOIUrl":"https://doi.org/10.1109/LRA.2025.3559841","url":null,"abstract":"Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5377-5384"},"PeriodicalIF":4.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856250","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

Topo-Field: Topometric Mapping With Brain-Inspired Hierarchical Layout-Object-Position Fields 拓扑场：基于大脑启发的分层布局-对象-位置场的拓扑映射

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-10 DOI: 10.1109/LRA.2025.3559836

Jiawei Hou;Wenhao Guan;Longfei Liang;Jianfeng Feng;Xiangyang Xue;Taiping Zeng

{"title":"Topo-Field: Topometric Mapping With Brain-Inspired Hierarchical Layout-Object-Position Fields","authors":"Jiawei Hou;Wenhao Guan;Longfei Liang;Jianfeng Feng;Xiangyang Xue;Taiping Zeng","doi":"10.1109/LRA.2025.3559836","DOIUrl":"https://doi.org/10.1109/LRA.2025.3559836","url":null,"abstract":"Mobile robots require comprehensive scene understanding to operate effectively in diverse environments, enriched with contextual information such as layouts, objects, and their relationships. Although advances like neural radiance fields (NeRFs) offer high-fidelity 3D reconstructions, they are computationally intensive and often lack efficient representations of traversable spaces essential for planning and navigation. In contrast, topological maps are computationally efficient but lack the semantic richness necessary for a more complete understanding of the environment. Inspired by a population code in the postrhinal cortex (POR) strongly tuned to spatial layouts over scene content rapidly forming a high-level cognitive map, this work introduces Topo-Field, a framework that integrates Layout-Object-Position (LOP) associations into a neural field and constructs a topometric map from this learned representation. LOP associations are modeled by explicitly encoding object and layout information, while a Large Foundation Model (LFM) technique allows for efficient training without extensive annotations. The topometric map is then constructed by querying the learned neural representation, offering both semantic richness and computational efficiency. Empirical evaluations in multi-room environments demonstrate the effectiveness of Topo-Field in tasks such as position attribute inference, query localization, and topometric planning, successfully bridging the gap between high-fidelity scene understanding and efficient robotic navigation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5385-5392"},"PeriodicalIF":4.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856378","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 Null Space Compliance Approach for Maintaining Safety and Tracking Performance in Human-Robot Interactions 在人机交互中保持安全和跟踪性能的无效空间遵从方法

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-10 DOI: 10.1109/LRA.2025.3559845

Zi-Qi Yang;Miaomiao Wang;Mehrdad R. Kermani

{"title":"A Null Space Compliance Approach for Maintaining Safety and Tracking Performance in Human-Robot Interactions","authors":"Zi-Qi Yang;Miaomiao Wang;Mehrdad R. Kermani","doi":"10.1109/LRA.2025.3559845","DOIUrl":"https://doi.org/10.1109/LRA.2025.3559845","url":null,"abstract":"In recent years, the focus on developing robot manipulators has shifted towards prioritizing safety in Human-Robot Interaction (HRI). Impedance control is a typical approach for interaction control in collaboration tasks. However, such a control approach has two main limitations: 1) the end-effector (EE)’s limited compliance to adapt to unknown physical interactions, and 2) inability of the robot body to compliantly adapt to unknown physical interactions. In this work, we present an approach to address these drawbacks. We introduce a modified Cartesian impedance control method combined with a Dynamical System (DS)-based motion generator, aimed at enhancing the interaction capability of the EE without compromising main task tracking performance. This approach enables human coworkers to interact with the EE on-the-fly, e.g. tool changeover, after which the robot compliantly resumes its task. Additionally, combining with a new null space impedance control method enables the robot body to exhibit compliant behaviour in response to interactions, avoiding serious injuries from accidental contact while mitigating the impact on main task tracking performance. Finally, we prove the passivity of the system and validate the proposed approach through comprehensive comparative experiments on a 7 Degree-of-Freedom (DOF) KUKA LWR IV+ robot.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5369-5376"},"PeriodicalIF":4.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856201","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

Fuzzy Ensembles of Reinforcement Learning Policies for Systems With Variable Parameters 变参数系统强化学习策略的模糊集成

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-10 DOI: 10.1109/LRA.2025.3559833

Abdel Gafoor Haddad;Mohammed B. Mohiuddin;Igor Boiko;Yahya Zweiri

{"title":"Fuzzy Ensembles of Reinforcement Learning Policies for Systems With Variable Parameters","authors":"Abdel Gafoor Haddad;Mohammed B. Mohiuddin;Igor Boiko;Yahya Zweiri","doi":"10.1109/LRA.2025.3559833","DOIUrl":"https://doi.org/10.1109/LRA.2025.3559833","url":null,"abstract":"This paper presents a novel approach to improving the generalization capabilities of reinforcement learning (RL) agents for robotic systems with varying physical parameters. We propose the Fuzzy Ensemble of RL policies (FERL), which enhances performance in environments where system parameters differ from those encountered during training. The FERL method selectively fuses aligned policies, determining their collective decision based on fuzzy memberships tailored to the current parameters of the system. Unlike traditional centralized training approaches that rely on shared experiences for policy updates, FERL allows for independent agent training, facilitating efficient parallelization. The effectiveness of FERL is demonstrated through extensive experiments, including a real-world trajectory tracking application in a quadrotor slung-load system. Our method improves the success rates by up to 15.6% across various simulated systems with variable parameters compared to the existing benchmarks of domain randomization and robust adaptive ensemble adversary RL. In the real-world experiments, our method achieves a 30% reduction in 3D position RMSE compared to individual RL policies. The results underscores FERL robustness and applicability to real robotic systems.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5361-5368"},"PeriodicalIF":4.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960640","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856379","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

Managing Conflicting Tasks in Heterogeneous Multi-Robot Systems Through Hierarchical Optimization 基于层次优化的异构多机器人系统冲突任务管理

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-10 DOI: 10.1109/LRA.2025.3559843

Davide De Benedittis;Manolo Garabini;Lucia Pallottino

引用次数: 0