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

Planning for Quasi-Static Manipulation Tasks via an Intrinsic Haptic Metric: A Book Insertion Case Study 规划准静态操作任务通过一个内在的触觉指标：一个图书插入案例研究

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3564707

Lin Yang;Sri Harsha Turlapati;Chen Lv;Domenico Campolo

{"title":"Planning for Quasi-Static Manipulation Tasks via an Intrinsic Haptic Metric: A Book Insertion Case Study","authors":"Lin Yang;Sri Harsha Turlapati;Chen Lv;Domenico Campolo","doi":"10.1109/LRA.2025.3564707","DOIUrl":"https://doi.org/10.1109/LRA.2025.3564707","url":null,"abstract":"Contact-rich manipulation often requires strategic interactions with objects, such as pushing to accomplish specific tasks. We propose a novel scenario where a robot inserts a book into a crowded shelf by pushing aside neighboring books to create space before slotting the new book into place. Classical planning algorithms fail in this context due to limited space and their tendency to avoid contact. Additionally, they do not handle indirectly manipulable objects or consider force interactions. Our key contributions are: <inline-formula><tex-math>$i)$</tex-math></inline-formula> reframing quasi-static manipulation as a planning problem on an implicit manifold derived from equilibrium conditions; <inline-formula><tex-math>$ii)$</tex-math></inline-formula> utilizing an intrinsic haptic metric instead of ad-hoc cost functions; and <inline-formula><tex-math>$iii)$</tex-math></inline-formula> proposing an adaptive algorithm that simultaneously updates robot states, object positions, contact points, and haptic distances. We evaluate our method on a crowded bookshelf insertion task, and it can be generally applied to rigid body manipulation tasks. We propose proxies to capture contact points and forces, with superellipses to represent objects. This simplified model guarantees differentiability. Our framework autonomously discovers strategic wedging-in policies while our simplified contact model achieves behavior similar to real world scenarios. We also vary the stiffness and initial positions to analyze our framework comprehensively.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6111-6118"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913574","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

Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking 触须启发触觉传感：精确水下接触跟踪的Sim2Real方法

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3564760

Hao Li;Chengyi Xing;Saad Khan;Miaoya Zhong;Mark R. Cutkosky

引用次数: 0

Optimized Design and Calibration of a Human-Eye-Sized Active Binocular Vision System Based on Spherical Parallel Mechanism 基于球面并联机构的人眼大小主动双目视觉系统优化设计与标定

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3564757

Kaifang Wang;DongDong Yang;Li Zhang;Jun Liu;Xiaolin Zhang

{"title":"Optimized Design and Calibration of a Human-Eye-Sized Active Binocular Vision System Based on Spherical Parallel Mechanism","authors":"Kaifang Wang;DongDong Yang;Li Zhang;Jun Liu;Xiaolin Zhang","doi":"10.1109/LRA.2025.3564757","DOIUrl":"https://doi.org/10.1109/LRA.2025.3564757","url":null,"abstract":"The Active Binocular Vision System (ABVS), resembling the human eye, demonstrates potential for improving visual perception in robotic systems, especially in dynamic and complex environments. In this letter, we present an optimized design of a three degree-of-freedom (DoF) Active Monocular Vision System (AMVS) based on a Spherical Parallel Manipulator (SPM). By combining two identical AMVS units, we form an ABVS, which has been successfully integrated into a humanoid robotic head. Due to the highly nonlinear kinematics of SPM and complex error coupling in its multi-link structure, traditional end-to-end neural network training methods are insufficient in accuracy and require large datasets. To address these challenges, we propose a two-branch optimization network that significantly improves calibration accuracy. Furthermore, we introduce a four-branch fine-tuning strategy that enables accurate kinematic models to be obtained with only a small amount of data from new AMVS devices. Experimental results demonstrate that the two-branch optimization network reduces rotational prediction error by 16% and translational error by 5% compared to a single-branch network. Furthermore, the four-branch fine-tuning network achieves comparable accuracy to a fully trained single-branch network using only 343 data points. Finally, our ABVS shows the capability to perform 3D visual tasks, such as stereo reconstruction during movement.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 7","pages":"6608-6615"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125441","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

Grasp-and-Classify Robotic Sorting With Grasping Rectangle Correction and Weighted Nearest-Neighbor Relation Network 基于抓取矩形校正和加权最近邻关系网络的抓取分类机器人分类

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3564781

Dongxiao Han;Yuwen Li

{"title":"Grasp-and-Classify Robotic Sorting With Grasping Rectangle Correction and Weighted Nearest-Neighbor Relation Network","authors":"Dongxiao Han;Yuwen Li","doi":"10.1109/LRA.2025.3564781","DOIUrl":"https://doi.org/10.1109/LRA.2025.3564781","url":null,"abstract":"Robotic sorting in cluttered environments still faces significant challenges, especially with resource-constrained hardware. Traditional detect-and-grasp workflows usually require extensive image collection and annotation for model training, which can become impractical when the categories of the sorted objects frequently change. To overcome this issue, this article proposes a grasp-and-classify robotic sorting method with deep learning-based object grasping and classification algorithms which can be deployed on resource-constrained hardware platforms. To do this, a Grasping Rectangle Correction (GRC) algorithm is incorporated to adjust the grasping poses generated from the Generative Residual Convolutional Neural Network (GR-ConvNetv2). Then, an efficient Weighted Nearest-Neighbor Relation Network (WNNRNet) is developed for few-shot object classification. This model unifies Deep Nearest Neighbor Neural Network (DN4) and Relation network to reduce overfitting through feature sharing, and the joint training with a weighted multi-task loss function can enhance the generalization capability of few-shot classification. Simulation tests have been carried out to validate the GRC and WNNRNet algorithms with Cornell, Jacquard, and MiniImageNet datasets. Finally, a robotic sorting system with a UR10 robot and a Kinect camera has been built to perform real-world sorting tests to demonstrate the effectiveness of the proposed method. Benefited from the efficient correction of the grasping pose with the GRC algorithm and the fact that the WNNRNet requires limited samples for training, the proposed method can be deployed on a consumer-level laptop for sorting stacked objects in scenarios with varying categories.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6103-6110"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913351","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

Should We Learn Contact-Rich Manipulation Policies From Sampling-Based Planners? 我们应该从基于抽样的规划者那里学习接触丰富的操作策略吗？

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3564701

Huaijiang Zhu;Tong Zhao;Xinpei Ni;Jiuguang Wang;Kuan Fang;Ludovic Righetti;Tao Pang

引用次数: 0

PLACE-LIO: Plane-Centric LiDAR-Inertial Odometry PLACE-LIO：以飞机为中心的激光雷达惯性里程计

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3564790

Linkun He;Bofeng Li;Guang'e Chen

引用次数: 0

Multi-Sensor Fusion for Quadruped Robot State Estimation Using Invariant Filtering and Smoothing 基于不变滤波和平滑的四足机器人多传感器融合状态估计

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3564711

Ylenia Nisticò;Hajun Kim;João Carlos Virgolino Soares;Geoff Fink;Hae-Won Park;Claudio Semini

{"title":"Multi-Sensor Fusion for Quadruped Robot State Estimation Using Invariant Filtering and Smoothing","authors":"Ylenia Nisticò;Hajun Kim;João Carlos Virgolino Soares;Geoff Fink;Hae-Won Park;Claudio Semini","doi":"10.1109/LRA.2025.3564711","DOIUrl":"https://doi.org/10.1109/LRA.2025.3564711","url":null,"abstract":"This letter introduces two multi-sensor state estimation frameworks for quadruped robots, built on the Invariant Extended Kalman Filter (InEKF) and Invariant Smoother (IS). The proposed methods, named E-InEKF and E-IS, fuse kinematics, IMU, LiDAR, and GPS data to mitigate position drift, particularly along the z-axis, a common issue in proprioceptive-based approaches. We derived observation models that satisfy group-affine properties to integrate LiDAR odometry and GPS into InEKF and IS. LiDAR odometry is incorporated using Iterative Closest Point (ICP) registration on a parallel thread, preserving the computational efficiency of proprioceptive-based state estimation. We evaluate E-InEKF and E-IS with and without exteroceptive sensors, benchmarking them against LiDAR-based odometry methods in indoor and outdoor experiments using the KAIST HOUND2 robot. Our methods achieve lower Relative Position Errors (RPE) and significantly reduce Absolute Trajectory Error (ATE), with improvements of up to 28% indoors and 40% outdoors compared to LIO-SAM and FAST-LIO2. Additionally, we compare E-InEKF and E-IS in terms of computational efficiency and accuracy.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6296-6303"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143938022","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

Enhancing Exploration With Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulation 利用扩散策略加强混合非策略RL的探索：在非抓握操作中的应用

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3564780

Huy Le;Tai Hoang;Miroslav Gabriel;Gerhard Neumann;Ngo Anh Vien

{"title":"Enhancing Exploration With Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulation","authors":"Huy Le;Tai Hoang;Miroslav Gabriel;Gerhard Neumann;Ngo Anh Vien","doi":"10.1109/LRA.2025.3564780","DOIUrl":"https://doi.org/10.1109/LRA.2025.3564780","url":null,"abstract":"Learning diverse policies for non-prehensile manipulation is essential for improving skill transfer and generalization to out-of-distribution scenarios. In this work, we enhance exploration through a two-fold approach within a hybrid framework that tackles both discrete and continuous action spaces. First, we model the continuous motion parameter policy as a diffusion model, and second, we incorporate this into a maximum entropy reinforcement learning framework that unifies both the discrete and continuous components. The discrete action space, such as contact point selection, is optimized through Q-value function maximization, while the continuous part is guided by a diffusion-based policy. This hybrid approach leads to a principled objective, where the maximum entropy term is derived as a lower bound using structured variational inference. We propose the Hybrid Diffusion Policy algorithm (<bold>HyDo</b>) and evaluate its performance on both simulation and zero-shot sim2real tasks. Our results show that HyDo encourages more diverse behavior policies, leading to significantly improved success rates across tasks - for example, increasing from 53% to 72% on a real-world 6D pose alignment task.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6143-6150"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925065","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

Road User Specific Trajectory Prediction in Mixed Traffic Using Map Data 基于地图数据的混合交通道路使用者特定轨迹预测

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3564746

Hidde J-H. Boekema;Emran Yasser Moustafa;Julian F.P. Kooij;Dariu M. Gavrila

{"title":"Road User Specific Trajectory Prediction in Mixed Traffic Using Map Data","authors":"Hidde J-H. Boekema;Emran Yasser Moustafa;Julian F.P. Kooij;Dariu M. Gavrila","doi":"10.1109/LRA.2025.3564746","DOIUrl":"https://doi.org/10.1109/LRA.2025.3564746","url":null,"abstract":"This paper studies road user trajectory prediction in mixed traffic, i.e. where vehicles and Vulnerable Road Users (VRUs, i.e. pedestrians, cyclists and other riders) closely share a common road space. We investigate if typical prediction components (scene graph representation, scene encoding, waypoint prediction, motion dynamics) should be specific to each road user class. Using the recent VRU-heavy View-of-Delft Prediction (VoD-P) dataset, we study several directions to improve the performance of the state-of-the-art map-based prediction models (PGP, TNT) in urban settings. First, we consider the use of class-specific map representations. Second, we investigate if the weights of different components of the model should be shared or separated by class. Finally, we augment VoD-P training data with automatically extracted trajectories from the 360-degree LiDAR scans by the recording vehicle. This data is made publicly available. We find that pre-training the model on auto-labels and making it class-specific leads to a reduction of up to 22.2<italic>%</i>, 20.0<italic>%</i>, and 18.2<italic>%</i> in minADE (<inline-formula><tex-math>$K = 10$</tex-math></inline-formula> samples) for pedestrians, cyclists, and vehicles, respectively.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6159-6166"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925067","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

EV-TTC: Event-Based Time to Collision Under Low Light Conditions EV-TTC：弱光条件下基于事件的碰撞时间

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-28 DOI: 10.1109/LRA.2025.3565150

Anthony Bisulco;Vijay Kumar;Kostas Daniilidis

{"title":"EV-TTC: Event-Based Time to Collision Under Low Light Conditions","authors":"Anthony Bisulco;Vijay Kumar;Kostas Daniilidis","doi":"10.1109/LRA.2025.3565150","DOIUrl":"https://doi.org/10.1109/LRA.2025.3565150","url":null,"abstract":"Rapid and accurate dense time-to-collision (TTC) estimation in resource-constrained, low-light environments is challenging for event-based camera systems. Fixed-time event representations like voxel grids face an inherent trade-off: larger temporal windows improve perception accuracy but increase storage demands, while smaller windows reduce storage at the cost of accuracy. We present a hardware-aware TTC estimation system designed for mobile robots, satisfying strict bandwidth, computation, and storage requirements. Our core innovation is a time-scale separation method for computing a multi-temporal scale event representation, achieving a latency of 3.3 ms at 75 Million Events per Second (MEPS). As part of this study, we developed Time-To-Collision/Event Flow (<inline-formula><tex-math>$T^{2}CEF$</tex-math></inline-formula>) a new high-temporal-resolution TTC dataset, using HD event cameras, with temporal estimates at least 7 times greater than existing event datasets such as MVSEC, DSEC, and VECtor via SE(3) interpolation. Our method outperforms existing approaches, reducing mean frame median TTC error by at least 20% compared to voxel grids on <inline-formula><tex-math>$T^{2}CEF$</tex-math></inline-formula>, and achieving an average 31% improvement over other baselines across multiple datasets. Our system runs in real-time on a Jetson Orin NX with just 9.5 ms latency at 141 Hz, outperforming all other methods on embedded hardware, making it ideal for mobile robots.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6151-6158"},"PeriodicalIF":4.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925319","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