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

Physics-Informed Neural Networks With Unscented Kalman Filter for Sensorless Joint Torque Estimation in Humanoid Robots 基于无气味卡尔曼滤波的物理信息神经网络在人形机器人无传感器关节力矩估计中的应用

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3562792

Ines Sorrentino;Giulio Romualdi;Lorenzo Moretti;Silvio Traversaro;Daniele Pucci

{"title":"Physics-Informed Neural Networks With Unscented Kalman Filter for Sensorless Joint Torque Estimation in Humanoid Robots","authors":"Ines Sorrentino;Giulio Romualdi;Lorenzo Moretti;Silvio Traversaro;Daniele Pucci","doi":"10.1109/LRA.2025.3562792","DOIUrl":"https://doi.org/10.1109/LRA.2025.3562792","url":null,"abstract":"This paper presents a novel framework for whole-body torque control of humanoid robots without joint torque sensors, designed for systems with electric motors and high-ratio harmonic drives. The approach integrates Physics-Informed Neural Networks (PINNs) for friction modeling and Unscented Kalman Filtering (UKF) for joint torque estimation, within a real-time torque control architecture. PINNs estimate nonlinear static and dynamic friction from joint and motor velocity readings, capturing effects like motor actuation without joint movement. The UKF utilizes PINN-based friction estimates as direct measurement inputs, improving torque estimation robustness. Experimental validation on the ergoCub humanoid robot demonstrates improved torque tracking accuracy, enhanced energy efficiency, and superior disturbance rejection compared to the state-of-the-art Recursive Newton-Euler Algorithm (RNEA), using a dynamic balancing experiment. The framework's scalability is shown by consistent performance across robots with similar hardware but different friction characteristics, without re-identification. Furthermore, a comparative analysis with position control highlights the advantages of the proposed torque control approach. The results establish the method as a scalable and practical solution for sensorless torque control in humanoid robots, ensuring torque tracking, adaptability, and stability in dynamic environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5705-5712"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883372","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

Emperror: A Flexible Generative Perception Error Model for Probing Self-Driving Planners 帝侯：用于探测自动驾驶计划者的柔性生成感知误差模型

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3562789

Niklas Hanselmann;Simon Doll;Marius Cordts;Hendrik P.A. Lensch;Andreas Geiger

{"title":"Emperror: A Flexible Generative Perception Error Model for Probing Self-Driving Planners","authors":"Niklas Hanselmann;Simon Doll;Marius Cordts;Hendrik P.A. Lensch;Andreas Geiger","doi":"10.1109/LRA.2025.3562789","DOIUrl":"https://doi.org/10.1109/LRA.2025.3562789","url":null,"abstract":"To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typically assume a setting in which the ground-truth world state is available as input. However, when deployed, planning needs to be robust to the long-tail of errors incurred by a noisy perception system, which is often neglected in evaluation. To address this, previous work has proposed drawing adversarial samples from a perception error model (PEM) mimicking the noise characteristics of a target object detector. However, these methods use simple PEMs that fail to accurately capture all failure modes of detection. In this letter, we present <sc>Emperror, a novel transformer-based generative PEM, apply it to stress-test an imitation learning (IL)-based planner and show that it imitates modern detectors more faithfully than previous work. Furthermore, it is able to produce realistic noisy inputs that increase the planner's collision rate by up to 85%, demonstrating its utility as a valuable tool for a more complete evaluation of self-driving planners.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5807-5814"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902626","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

Stereo Hand-Object Reconstruction for Human-to-Robot Handover 面向人机切换的立体手-物重建

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3562790

Yik Lung Pang;Alessio Xompero;Changjae Oh;Andrea Cavallaro

引用次数: 0

Tree-Based Grafting Approach for Bidirectional Motion Planning With Local Subsets Optimization 基于树的局部子集优化双向运动规划方法

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-18 DOI: 10.1109/LRA.2025.3562369

Liding Zhang;Yao Ling;Zhenshan Bing;Fan Wu;Sami Haddadin;Alois Knoll

{"title":"Tree-Based Grafting Approach for Bidirectional Motion Planning With Local Subsets Optimization","authors":"Liding Zhang;Yao Ling;Zhenshan Bing;Fan Wu;Sami Haddadin;Alois Knoll","doi":"10.1109/LRA.2025.3562369","DOIUrl":"https://doi.org/10.1109/LRA.2025.3562369","url":null,"abstract":"Bidirectional motion planning often reduces planning time compared to its unidirectional counterparts. It requires connecting the forward and reverse search trees to form a continuous path. However, this process could fail and restart the asymmetric bidirectional search due to the limitations of lazy-reverse search. To address this challenge, we propose Greedy GuILD Grafting Trees (G3T*), a novel path planner that grafts invalid edge connections at both ends to re-establish tree-based connectivity, enabling rapid path convergence. G3T* employs a greedy approach using the minimum Lebesgue measure of guided incremental local densification (GuILD) subsets to optimize paths efficiently. Furthermore, G3T* dynamically adjusts the sampling distribution between the informed set and GuILD subsets based on historical and current cost improvements, ensuring asymptotic optimality. These features enhance the forward search's growth towards the reverse tree, achieving faster convergence and lower solution costs. Benchmark experiments across dimensions from <inline-formula><tex-math>$mathbb {R}^{2}$</tex-math></inline-formula> to <inline-formula><tex-math>$mathbb {R}^{8}$</tex-math></inline-formula> and real-world robotic evaluations demonstrate G3T*’s superior performance compared to existing single-query sampling-based planners.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5815-5822"},"PeriodicalIF":4.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902628","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

LLM-Based Multi-Agent Decision-Making: Challenges and Future Directions 基于llm的多智能体决策：挑战与未来方向

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-18 DOI: 10.1109/LRA.2025.3562371

Chuanneng Sun;Songjun Huang;Dario Pompili

引用次数: 0

Waliner: Lightweight and Resilient Plugin Mapping Method With Wall Features for Visually Challenging Indoor Environments Waliner：轻量级和弹性插件映射方法，具有视觉挑战性的室内环境的墙壁特征

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-18 DOI: 10.1109/LRA.2025.3562370

DongKi Noh;Byunguk Lee;Hanngyoo Kim;SeungHwan Lee;HyunSung Kim;JuWon Kim;Jeongsik Choi;SeungMin Baek

{"title":"Waliner: Lightweight and Resilient Plugin Mapping Method With Wall Features for Visually Challenging Indoor Environments","authors":"DongKi Noh;Byunguk Lee;Hanngyoo Kim;SeungHwan Lee;HyunSung Kim;JuWon Kim;Jeongsik Choi;SeungMin Baek","doi":"10.1109/LRA.2025.3562370","DOIUrl":"https://doi.org/10.1109/LRA.2025.3562370","url":null,"abstract":"Vision-based indoor navigation systems have been proposed previously for service robots. However, in real-world scenarios, many of these approaches remain vulnerable to visually challenging environments such as white walls. In-home service robots, which are mass-produced, require affordable sensors and processors. Therefore, this letter presents a lightweight and resilient plugin mapping method called <italic>Waliner, using an RGB-D sensor and an embedded processor equipped with a neural processing unit (NPU). <italic>Waliner can be easily implemented in existing algorithms and enhances the accuracy and robustness of 2D/3D mapping in visually challenging environments with minimal computational overhead by leveraging <bold>a) structural building components, such as walls; <bold>b) the Manhattan world assumption; and <bold>c) an extended Kalman filter-based pose estimation and map management technique to maintain reliable mapping performance under varying lighting and featureless conditions. As verified in various real-world in-home scenes, the proposed method yields over a 5 % improvement in mapping consistency as measured by the map similarity index (MSI) while using minimal resources.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5799-5806"},"PeriodicalIF":4.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900581","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 Highly Maneuverable Flying Squirrel Drone With Agility-Improving Foldable Wings 具有可折叠机翼的高机动性鼯鼠无人机

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-18 DOI: 10.1109/LRA.2025.3562372

Dohyeon Lee;Jun-Gill Kang;Soohee Han

{"title":"A Highly Maneuverable Flying Squirrel Drone With Agility-Improving Foldable Wings","authors":"Dohyeon Lee;Jun-Gill Kang;Soohee Han","doi":"10.1109/LRA.2025.3562372","DOIUrl":"https://doi.org/10.1109/LRA.2025.3562372","url":null,"abstract":"Drones, like most airborne aerial vehicles, face inherent disadvantages in achieving agile flight due to their limited thrust capabilities. These physical constraints cannot be fully addressed through advancements in control algorithms alone. Drawing inspiration from the winged flying squirrel, this letter proposes a highly maneuverable drone with agility-enhancing foldable wings. The additional air resistance generated by appropriately deploying these wings significantly improves the tracking performance of the proposed “flying squirrel” drone. By leveraging collaborative control between the conventional propeller system and the foldable wings—coordinated through the Thrust-Wing Coordination Control (TWCC) framework—the controllable acceleration set is expanded, allowing for the production of abrupt vertical forces unachievable with traditional wingless drones. The complex aerodynamics of the foldable wings are captured using a physics-assisted recurrent neural network (paRNN), which calibrates the angle of attack (AOA) to align with the real-world aerodynamic behavior of the wings. The model is trained on real-world flight data and incorporates flat-plate aerodynamic principles. Experimental results demonstrate that the proposed flying squirrel drone achieves a 13.1<inline-formula><tex-math>${%}$</tex-math></inline-formula> improvement in tracking performance, as measured by root mean square error (RMSE), compared to a conventional wingless drone.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5783-5790"},"PeriodicalIF":4.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900417","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

II-NVM: Enhancing Map Accuracy and Consistency With Normal Vector-Assisted Mapping nvm：用法向量辅助映射增强地图精度和一致性

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-16 DOI: 10.1109/LRA.2025.3561568

Chengwei Zhao;Yixuan Li;Yina Jian;Jie Xu;Linji Wang;Yongxin Ma;Xinglai Jin

{"title":"II-NVM: Enhancing Map Accuracy and Consistency With Normal Vector-Assisted Mapping","authors":"Chengwei Zhao;Yixuan Li;Yina Jian;Jie Xu;Linji Wang;Yongxin Ma;Xinglai Jin","doi":"10.1109/LRA.2025.3561568","DOIUrl":"https://doi.org/10.1109/LRA.2025.3561568","url":null,"abstract":"SLAM technology plays a crucial role in indoor mapping and localization. A common challenge in indoor environments is the “double-sided mapping issue”, where closely positioned walls, doors, and other surfaces are mistakenly identified as a single plane, significantly hindering map accuracy and consistency. To addressing this issue this letter introduces a SLAM approach that ensures accurate mapping using normal vector consistency. We enhance the voxel map structure to store both point cloud data and normal vector information, enabling the system to evaluate consistency during nearest neighbor searches and map updates. This process distinguishes between the front and back sides of surfaces, preventing incorrect point-to-plane constraints. Moreover, we implement an adaptive radius KD-tree search method that dynamically adjusts the search radius based on the local density of the point cloud, thereby enhancing the accuracy of normal vector calculations. To further improve real-time performance and storage efficiency, we incorporate a Least Recently Used (LRU) cache strategy, which facilitates efficient incremental updates of the voxel map. The <uri>code</uri> is released as open-source and validated in both simulated environments and real indoor scenarios. Experimental results demonstrate that this approach effectively resolves the “double-sided mapping issue” and significantly improves mapping precision. Additionally, we have developed and open-sourced the first simulation and real-world dataset specifically tailored for the “double-sided mapping issue”.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5465-5472"},"PeriodicalIF":4.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888448","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

MR-COGraphs: Communication-Efficient Multi-Robot Open-Vocabulary Mapping System via 3D Scene Graphs 基于三维场景图的高效通信多机器人开放词汇映射系统

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-16 DOI: 10.1109/LRA.2025.3561569

Qiuyi Gu;Zhaocheng Ye;Jincheng Yu;Jiahao Tang;Tinghao Yi;Yuhan Dong;Jian Wang;Jinqiang Cui;Xinlei Chen;Yu Wang

{"title":"MR-COGraphs: Communication-Efficient Multi-Robot Open-Vocabulary Mapping System via 3D Scene Graphs","authors":"Qiuyi Gu;Zhaocheng Ye;Jincheng Yu;Jiahao Tang;Tinghao Yi;Yuhan Dong;Jian Wang;Jinqiang Cui;Xinlei Chen;Yu Wang","doi":"10.1109/LRA.2025.3561569","DOIUrl":"https://doi.org/10.1109/LRA.2025.3561569","url":null,"abstract":"Collaborative perception in unknown environments is crucial for multi-robot systems. With the emergence of foundation models, robots can now not only perceive geometric information but also achieve open-vocabulary scene understanding. However, existing map representations that support open-vocabulary queries often involve large data volumes, which becomes a bottleneck for multi-robot transmission in communication-limited environments. To address this challenge, we develop a method to construct a graph-structured 3D representation called COGraph, where nodes represent objects with semantic features and edges capture their spatial adjacency relationships. Before transmission, a data-driven feature encoder is applied to compress the feature dimensions of the COGraph. Upon receiving COGraphs from other robots, the semantic features of each node are recovered using a decoder. We also propose a feature-based approach for place recognition and translation estimation, enabling the merging of local COGraphs into a unified global map. We validate our framework on two realistic datasets and the real-world environment. The results demonstrate that, compared to existing baselines for open-vocabulary map construction, our framework reduces the data volume by over 80% while maintaining mapping and query performance without compromise.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5713-5720"},"PeriodicalIF":4.6,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883373","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

Effect of Reduced-Order Modelling on Passivity and Rendering Performance Analyses of Series Elastic Actuation 降阶建模对串联弹性作动机构无源性及渲染性能分析的影响

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-16 DOI: 10.1109/LRA.2025.3561564

Celal Umut Kenanoglu;Volkan Patoglu

引用次数: 0