IEEE Robotics and Automation Letters最新文献

ZORMS-LfD: Learning From Demonstrations With Zeroth-Order Random Matrix Search ZORMS-LfD：从零阶随机矩阵搜索的演示中学习

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592144

Olivia Dry;Timothy L. Molloy;Wanxin Jin;Iman Shames

{"title":"ZORMS-LfD: Learning From Demonstrations With Zeroth-Order Random Matrix Search","authors":"Olivia Dry;Timothy L. Molloy;Wanxin Jin;Iman Shames","doi":"10.1109/LRA.2025.3592144","DOIUrl":"https://doi.org/10.1109/LRA.2025.3592144","url":null,"abstract":"We propose Zeroth-Order Random Matrix Search for Learning from Demonstrations (ZORMS-LfD). ZORMS-LfD enables the costs, constraints, and dynamics of constrained optimal control problems, in both continuous and discrete time, to be learned from expert demonstrations without requiring smoothness of the learning-loss landscape. In contrast, existing state-of-the-art first-order methods require the existence and computation of gradients of the costs, constraints, dynamics, and learning loss with respect to states, controls and/or parameters. Most existing methods are also tailored to discrete time, with constrained problems in continuous time receiving only cursory attention. We demonstrate that ZORMS-LfD matches or surpasses the performance of state-of-the-art methods in terms of both learning loss and compute time across a variety of benchmark problems. On unconstrained continuous-time benchmark problems, ZORMS-LfD achieves similar loss performance to state-of-the-art first-order methods with an over 80% reduction in compute time. On constrained continuous-time benchmark problems where there is no specialized state-of-the-art method, ZORMS-LfD is shown to outperform the commonly used gradient-free Nelder-Mead optimization method. We illustrate the practicality of ZORMS-LfD on a human motion dataset, and derive complexity bounds for it on problems with Lipschitz continuous (but potentially nondifferentiable) loss.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 9","pages":"9208-9215"},"PeriodicalIF":5.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758393","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

Anticipating Degradation: A Predictive Approach to Fault Tolerance in Robot Swarms 预测退化：机器人群体容错的预测方法

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592063

James O'Keeffe

引用次数: 0

Geo-LSTM: A Geometry and Temporal Feature Fusion Algorithm for Multi-Sensor 3D Localization Geo-LSTM：一种多传感器三维定位的几何与时间特征融合算法

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592087

Kai Li;Le Bao;Wansoo Kim

{"title":"Geo-LSTM: A Geometry and Temporal Feature Fusion Algorithm for Multi-Sensor 3D Localization","authors":"Kai Li;Le Bao;Wansoo Kim","doi":"10.1109/LRA.2025.3592087","DOIUrl":"https://doi.org/10.1109/LRA.2025.3592087","url":null,"abstract":"Accurate three-dimensional (3D) localization is critical for robust human-robot collaboration (HRC) in dynamic indoor environments. However, realizing high-precision localization in complex scenarios still faces challenges such as multipath effects, field-of-view occlusion, etc. To address these limitations, we propose Geo-LSTM, a geometry-constrained long short-term memory (LSTM) framework that integrates ultra-wideband (UWB) sensors, inertial measurement unit (IMU), and barometric pressure (BMP) sensors. First, a Simplified Geometric Localization (SGL) algorithm is proposed, which uses dual-BMP sensors and IMU sensor to obtain precise height information and utilizes the geometric relationships between the UWB tag and anchors to compute an initial location estimate, serving as a priori input for the Geo-LSTM network. This Geo-LSTM algorithm then incorporates multi-source geometric information to extract time-series features from the UWB ranging data and the tag's a priori location, further enhancing 3D localization accuracy. The experimental results from the cluttered indoor environments, including real-world HRC tasks with occlusions, show that the Geo-LSTM algorithm achieves an average 3D localization root mean square error (RMSE) of 0.103 m, representing improvements of 38.60% and 31.20% over the weighted least squares (WLS) method and the range-based LSTM algorithm, respectively. These results demonstrate Geo-LSTM's potential for reliable multi-sensor 3D localization in HRC applications.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 9","pages":"9128-9135"},"PeriodicalIF":5.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758238","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

UltraVPR: Unsupervised Lightweight Rotation- Invariant Aerial Visual Place Recognition 无监督轻量级旋转-不变航空视觉位置识别

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592075

Chao Chen;Chunyu Li;Mengfan He;Jun Wang;Fei Xing;Ziyang Meng

{"title":"UltraVPR: Unsupervised Lightweight Rotation- Invariant Aerial Visual Place Recognition","authors":"Chao Chen;Chunyu Li;Mengfan He;Jun Wang;Fei Xing;Ziyang Meng","doi":"10.1109/LRA.2025.3592075","DOIUrl":"https://doi.org/10.1109/LRA.2025.3592075","url":null,"abstract":"Aerial Visual Place Recognition (VPR) is critical for Autonomous Aerial Vehicles (UAVs) localization, especially in environments with unstable or unavailable GPS signals. While neural network-based VPR methods have become mainstream, they face significant challenges on UAV platforms. Traditional CNN-based VPR models are highly sensitive to image rotation, degrading their performance in aerial-domain environments. Meanwhile, Transformer-based models have high computational complexity, making them less suitable for resource-constrained UAVs. In this letter, we propose a lightweight, rotation-invariant aerial VPR method. Our approach combines a rotation-equivariant backbone network with a rotation-invariant aggregation layer to ensure descriptor consistency across different orientations. Additionally, we propose an unsupervised training strategy that constructs higher-dimensional descriptors to optimize the model, while maintaining the lower descriptor dimensionality during application. Experimental results show that our method outperforms state-of-the-art methods across multiple aerial VPR datasets.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 9","pages":"9096-9103"},"PeriodicalIF":5.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758394","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

Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion Over Real-World Uneven Terrain 学会教：在现实世界不平坦的地形上的人形运动的样本高效特权学习

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592131

Feiyang Wu;Xavier Nal;Jaehwi Jang;Wei Zhu;Zhaoyuan Gu;Anqi Wu;Ye Zhao

引用次数: 0

Efficient Multi-Robot Task and Path Planning in Large-Scale Cluttered Environments 大规模杂乱环境下的高效多机器人任务与路径规划

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592146

Gang Xu;Yuchen Wu;Sheng Tao;Yifan Yang;Tao Liu;Tao Huang;Huifeng Wu;Yong Liu

{"title":"Efficient Multi-Robot Task and Path Planning in Large-Scale Cluttered Environments","authors":"Gang Xu;Yuchen Wu;Sheng Tao;Yifan Yang;Tao Liu;Tao Huang;Huifeng Wu;Yong Liu","doi":"10.1109/LRA.2025.3592146","DOIUrl":"https://doi.org/10.1109/LRA.2025.3592146","url":null,"abstract":"As the potential of multi-robot systems continues to be explored and validated across various real-world applications, such as package delivery, search and rescue, and autonomous exploration, the need to improve the efficiency and quality of task and path planning has become increasingly urgent, particularly in large-scale, obstacle-rich environments. To this end, this letter investigates the problem of multi-robot task and path planning (MRTPP) in large-scale cluttered scenarios. Specifically, we first propose an obstacle-vertex search (OVS) path planner that quickly constructs the cost matrix of collision-free paths for multi-robot task planning, ensuring the rationality of task planning in obstacle-rich environments. Furthermore, we introduce an efficient auction-based method for solving the MRTPP problem by incorporating a novel memory-aware strategy, aiming to minimize the maximum travel cost among robots for task visits. The proposed method effectively improves computational efficiency while maintaining solution quality in the multi-robot task planning problem. Finally, we demonstrated the effectiveness and practicality of the proposed method through extensive benchmark comparisons.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 9","pages":"9112-9119"},"PeriodicalIF":5.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758193","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

Fault Handling in Robotic Manipulation Tasks for Model Predictive Interaction Control 模型预测交互控制中机器人操作任务的故障处理

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592069

Tim Goller;Valentin Hopf;Andreas Völz;Knut Graichen

引用次数: 0

Self-Supervised Underwater Monocular Depth Estimation Informed by Multi-Physics Processes 基于多物理场过程的自监督水下单目深度估计

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592137

Fengqi Xiao;Juntian Qu

{"title":"Self-Supervised Underwater Monocular Depth Estimation Informed by Multi-Physics Processes","authors":"Fengqi Xiao;Juntian Qu","doi":"10.1109/LRA.2025.3592137","DOIUrl":"https://doi.org/10.1109/LRA.2025.3592137","url":null,"abstract":"Depth information is crucial for underwater robotic detection and navigation tasks. However, the underwater imaging environment is complex and variable. The images captured by robots are typically sequences or videos with uniform scene content, and the ground-truth of depth is difficult to obtain. This challenge hinders the generalization of existing self-supervised monocular depth estimation (SMDE) schemes for practical underwater detection applications. To address this issue, we propose an SMDE method for underwater images informed by the physical process of optical degradation. Specifically, we developed a further degradation process for underwater images, which can constrain the image restoration process to solve the attenuation coefficient and depth map, and then combine it with the ego-motion based framework to form a self-supervised learning closed loop. Guided by inherent optical properties, this closed-loop can learn depth cues from the underwater image formation model and the geometric relationships involved in view transformation. Experiments demonstrate that the proposed method is reduced by about 9.1% in RMSE index and improved by about 3.5% in threshold accuracy compared with the SOTA method and can adapt to various underwater robot detection scenarios.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 9","pages":"9144-9151"},"PeriodicalIF":5.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758403","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

Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies 混合现实数字孪生：利用物理和虚拟世界实现多智能体强化学习策略的混合Sim2Real过渡

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592085

Chinmay Samak;Tanmay Samak;Venkat Krovi

{"title":"Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies","authors":"Chinmay Samak;Tanmay Samak;Venkat Krovi","doi":"10.1109/LRA.2025.3592085","DOIUrl":"https://doi.org/10.1109/LRA.2025.3592085","url":null,"abstract":"Multi-agent reinforcement learning (MARL) for cyber-physical vehicle systems usually requires a significantly long training time due to their inherent complexity. Furthermore, deploying the trained policies in the real world demands a feature-rich environment along with multiple physical embodied agents, which may not be feasible due to monetary, physical, energy, or safety constraints. This work seeks to address these pain points by presenting a mixed-reality (MR) digital twin (DT) framework capable of: (i) boosting training speeds by selectively scaling parallelized simulation workloads on-demand, and (ii) immersing the MARL policies across hybrid simulation-to-reality (sim2real) experiments. The viability and performance of the proposed framework are highlighted through two representative use cases, which cover cooperative as well as competitive classes of MARL problems. We study the effect of: (i) agent and environment parallelization on training time, and (ii) systematic domain randomization on zero-shot sim2real transfer, across both case studies. Results indicate up to 76.3% reduction in training time with the proposed parallelization scheme and sim2real gap as low as 2.9% using the proposed deployment method.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 9","pages":"9040-9047"},"PeriodicalIF":5.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739974","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

Decision-Making for Autonomous Driving via a Coupled Reinforcement Learning Network Combined With Risk Assessment 基于风险评估和耦合强化学习网络的自动驾驶决策

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-07-23 DOI: 10.1109/LRA.2025.3592081

Chuan Hu;Yixun Niu;Hao Jiang;Xi Zhang;Xin Cheng

{"title":"Decision-Making for Autonomous Driving via a Coupled Reinforcement Learning Network Combined With Risk Assessment","authors":"Chuan Hu;Yixun Niu;Hao Jiang;Xi Zhang;Xin Cheng","doi":"10.1109/LRA.2025.3592081","DOIUrl":"https://doi.org/10.1109/LRA.2025.3592081","url":null,"abstract":"The realization of autonomous driving(AV) is closely linked to the development of intelligent decision-making modules that can operate safely in dynamic, uncertain environments. To address issues such as delayed response and poor coupling in highway scenarios, this letter proposes a hierarchical Coupled Decision-Making (CDM) framework. The CDM framework separates high-level intention planning from low-level behavior control. The upper layer uses DE-D3QN, enhanced with episodic memory buffer (EMB) and experience replay buffer (ERB), to improve learning efficiency in large-scale state spaces. The lower layer employs BF-TD3 with a barrier function to generate continuous, risk-aware control actions. Additionally, a probabilistic risk model combining lane speed gain, risk indicators, and Bayesian estimation enables adaptive evaluation of surrounding vehicle impact. To verify the contribution of each component, ablation studies are conducted on the virtual distance model and barrier function. Results show that CDM achieves better training stability, decision robustness, and a more favorable balance between risk and efficiency compared to baselines, while aligning decisions with driving expectations.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 9","pages":"8994-9001"},"PeriodicalIF":5.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751087","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