IEEE Robotics and Automation Letters最新文献_第9页

VLA-Touch: Enhancing Vision-Language-Action Model With Dual-Level Tactile Feedback 用双水平触觉反馈增强视觉-语言-动作模型

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.2026.3692345

Jianxin Bi;Kevin Yuchen Ma;Ce Hao;Mike Shou Zheng;Harold Soh

引用次数: 0

Hybrid Consistency Policy: Decoupling Multi-Modal Diversity and Real-Time Efficiency in Robotic Manipulation 混合一致性策略：解耦多模态多样性和机器人操作的实时效率

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/LRA.2026.3701559

Qianyou Zhao;Yuliang Shen;Xuanran Zhai;Duidi Wu;Jin Qi;Ce Hao;Jie Hu;Qiaojun Yu

{"title":"Hybrid Consistency Policy: Decoupling Multi-Modal Diversity and Real-Time Efficiency in Robotic Manipulation","authors":"Qianyou Zhao;Yuliang Shen;Xuanran Zhai;Duidi Wu;Jin Qi;Ce Hao;Jie Hu;Qiaojun Yu","doi":"10.1109/LRA.2026.3701559","DOIUrl":"https://doi.org/10.1109/LRA.2026.3701559","url":null,"abstract":"In visuomotor policy learning, diffusion-based imitation learning has become widely adopted for its ability to capture diverse behaviors. However, approaches built on ordinary and stochastic denoising processes struggle to jointly achieve fast sampling and strong multi-modality. To address these challenges, we propose the Hybrid Consistency Policy (HCP). HCP runs a short stochastic prefix up to an adaptive switch time, and then applies a one-step consistency jump to produce the final action. To align this one-jump generation, HCP performs time-varying consistency distillation that combines a trajectory-consistency objective to keep neighboring predictions coherent and a denoising-matching objective to improve local fidelity. In both simulation and on a real robot, HCP with 25 SDE steps plus one jump approaches the 80-step DDPM teacher in accuracy and mode coverage while significantly reducing latency. These results show that multi-modality does not require slow inference, and a switch time decouples mode retention from speed. It yields a practical accuracy–efficiency trade-off for robot policies.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8825-8832"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148628072","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

Event-Triggered MPC With Linear Inter-Event Control for AV Path Tracking 带有线性事件间控制的事件触发MPC自动驾驶汽车路径跟踪

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.2026.3692329

Zhaodong Zhou;Jun Chen

{"title":"Event-Triggered MPC With Linear Inter-Event Control for AV Path Tracking","authors":"Zhaodong Zhou;Jun Chen","doi":"10.1109/LRA.2026.3692329","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692329","url":null,"abstract":"Model predictive control (MPC) is widely used for autonomous vehicle path tracking due to its ability to handle system constraints and optimize performance over a prediction horizon. However, frequent online optimization imposes high computational demands, making the real-time implementation of MPC challenging. Event-triggered MPC aims to solve this issue by updating control actions only when a predefined condition is met, but it executes precomputed control sequences in an open-loop fashion between events, potentially allowing errors to accumulate. This letter proposes an event-triggered MPC framework integrated with a linear inter-event control mechanism to address this limitation. The proposed inter-event controller applies a least-squares-based linear model to generate control inputs in real-time during inter-event periods, enabling continuous feedback corrections. Experimental results on a Quanser QCar2 platform demonstrate that the proposed approach improves tracking accuracy by 10% while significantly reducing the number of MPC optimizations compared to standard event-triggered MPC, offering an efficient solution for real-time path tracking problem.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7868-7875"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510477","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

FlowPed: Proprioceptive State Fusion and Efficient Diffusion for Pedestrian Trajectory Prediction in Manufacturing Plants 流动：制造工厂行人轨迹预测的本体感觉状态融合和有效扩散

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/LRA.2026.3701124

Shaoze Yang;Shreyas Bhat;Doo Won Han;Al Salour;Terra Stroup;Rumzi Barakat;Paul Pridham;Yili Liu;X. Jessie Yang

{"title":"FlowPed: Proprioceptive State Fusion and Efficient Diffusion for Pedestrian Trajectory Prediction in Manufacturing Plants","authors":"Shaoze Yang;Shreyas Bhat;Doo Won Han;Al Salour;Terra Stroup;Rumzi Barakat;Paul Pridham;Yili Liu;X. Jessie Yang","doi":"10.1109/LRA.2026.3701124","DOIUrl":"https://doi.org/10.1109/LRA.2026.3701124","url":null,"abstract":"Autonomous Guided Vehicles (AGVs) must anticipate human movement to operate safely in manufacturing plants. Unlike outdoor crowd settings, shop floors offer rich, reliable environmental priors (CAD/HD maps, stations, sidewalks, flow rules), but little shareable trajectory data and tight latency budgets. We present <bold>FlowPed</b>, a trajectory forecasting framework tailored to this regime. FlowPed (i) fuses training-time privileged plant signals, including finite-automaton (FAM) motion states and geometry-aware features to stabilize learning under data scarcity; (ii) abstracts behavior via a VQ-VAE that discovers discrete latent “actions” and augments a gated residual + Transformer encoder to form a compact condition vector; and (iii) generates futures with an Elucidated Diffusion Model (EDM) implemented as a Diffusion Transformer (DiT) with few-step Karras sampling for real-time inference. We pretrained on public pedestrian repositories using only generic kinematics and fine-tuned on a VR shop-floor dataset (30 participants; 10 hours) instrumented with worker pose/gaze and AGV state. In ablations, FlowPed achieved 2.5× faster inference than a 5-step flow-matching baseline on AGV-class hardware (0.022 s vs. 0.056 s per forecast on an RTX A2000) while maintaining or improving accuracy (e.g., 0.6% <inline-formula><tex-math>$L_{text{2 mean}}$</tex-math></inline-formula>, 25.6% <inline-formula><tex-math>$L_{text{2 max}}$</tex-math></inline-formula> at 4s). Pre-training yielded consistent gains across decoder variants (4.2–6.4% <inline-formula><tex-math>$L_{text{2 mean}}$</tex-math></inline-formula>, 3.7–5.0% <inline-formula><tex-math>$L_{text{2 max}}$</tex-math></inline-formula>). The results indicate that the combination of plant priors, discrete latent actions, and few-step diffusion enables accurate, low-latency pedestrian prediction suitable for embedded AGV stacks in environment-rich, but data-scarce factories.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8888-8895"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442520","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

Geometrically Consistent Tactile Servoing via Hybrid Force-Position Control at the Center of Pressure 通过压力中心的混合力-位置控制实现几何一致的触觉伺服

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.2026.3699162

Sébastien Kleff;Lucas Joseph;Vincent Padois

{"title":"Geometrically Consistent Tactile Servoing via Hybrid Force-Position Control at the Center of Pressure","authors":"Sébastien Kleff;Lucas Joseph;Vincent Padois","doi":"10.1109/LRA.2026.3699162","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699162","url":null,"abstract":"In robotics, traditional force control lacks local contact information. Tactile sensors provide rich feedback on physical interaction, but remain difficult to integrate consistently into real-time control loops. In this paper, we show that hybrid force–position control, when expressed at the Center of Pressure (CoP), becomes geometrically consistent with tactile sensing. Based on this insight, we formulate a tactile servoing approach that allows explicit control of contact pose and force at the CoP. Unlike conventional tactile servoing techniques, which are tightly coupled to a specific sensor and often treat the contact wrench as a disturbance to be rejected, our approach relies on a generic and physically grounded feature space. We derive a hybrid force-position control law based on the Jacobian at the CoP that naturally decouples force and motion subspaces, ensuring geometric consistency during contact interaction. Real-world experiments on a robotic manipulator demonstrate robust contact maintenance and improved force tracking compared to standard image-based and pose-based controllers in a physical interaction regulation task.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8928-8935"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442862","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

Social Robot Navigation Under Kinodynamic Constraints Using Learning-Informed Sampling for Indoor Environments 基于学习信息采样的室内环境动力学约束下的社交机器人导航

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.2026.3699250

Steven Silva;Victor Romero-Cano;Juan David Hernández

{"title":"Social Robot Navigation Under Kinodynamic Constraints Using Learning-Informed Sampling for Indoor Environments","authors":"Steven Silva;Victor Romero-Cano;Juan David Hernández","doi":"10.1109/LRA.2026.3699250","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699250","url":null,"abstract":"With the inclusion of robots in social spaces to assist in service tasks (e.g. waitering and guiding people), having robots that move in a socially acceptable manner has become a requirement. In such dynamic environments, robots might show sudden stops and unexpected changes in acceleration, which can negatively affect the robots' predictability and legibility. Previous work has attempted to reduce such acceleration changes by generating paths that are both kinodynamically feasible and socially acceptable. However, most of these approaches not only fail to guarantee path optimality but also involve a computational cost that limits the robot's online navigation capabilities. In this paper, we present a social robot navigation (SRN) framework designed to overcome these limitations through three blocks: 1) world representation, 2) multilayered path planning, and 3) path-following control. Our approach incorporates a CNN-driven informed sampling strategy, which improves the robot's path planner performance to meet online computation constraints. We extensively benchmark our framework against state-of-the-art approaches in a variety of simulated scenarios, and demonstrate its feasibility with the Reachy robot in real-world tests.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"9000-9007"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443144","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

ESF-3Det: Edge-Semantic Fusion With Uncertainty Awareness for Robust LiDAR-Based 3D Object Detection ESF-3Det：基于不确定性感知的边缘语义融合鲁棒lidar三维目标检测

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.2026.3699156

Huishan Wang;Jie Ma;Jianlei Zhang;Fangwei Chen

{"title":"ESF-3Det: Edge-Semantic Fusion With Uncertainty Awareness for Robust LiDAR-Based 3D Object Detection","authors":"Huishan Wang;Jie Ma;Jianlei Zhang;Fangwei Chen","doi":"10.1109/LRA.2026.3699156","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699156","url":null,"abstract":"LiDAR-based 3D object detection is fundamental for real-world perception, providing accurate geometric information for downstream tasks. However, performance often degrades under challenging conditions such as occlusion, sparse point clouds, and small-scale objects. To address these issues, we propose ESF-3Det, a unified Edge–Semantic Fusion framework that jointly models geometric structure, semantic context, and predictive uncertainty for precise boundary localization. The framework comprises three core components: an Edge Pyramid Network (EPN) that extracts hierarchical boundary features from bird’s-eye view representations; an Edge-Guided Attention (EGA) module that aligns semantic features with geometric contours to mitigate semantic drift; and an Uncertainty-Aware Fusion (UAF) mechanism that adaptively integrates multi-level features in ambiguous regions based on semantic entropy and edge cues. By jointly leveraging boundary cues, semantic guidance, and uncertainty modeling, ESF-3Det produces boundary-consistent and uncertainty-aware representations, enhancing detection robustness. Extensive experiments on the KITTI and Waymo datasets demonstrate consistent improvements, including a 6.55% AP gain for heavily occluded pedestrians, validating the effectiveness and generalizability of the proposed framework.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8495-8502"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627605","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

HCDiff: Hierarchical Latent Constraint-Projected Diffusion Framework for Deformable Linear Objects Manipulation in Cluttered Environments 混沌环境下可变形线性对象操作的层次潜约束-投影扩散框架

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.2026.3699256

Yixiong Du;Lu Liu;Zhongkai Zhang

{"title":"HCDiff: Hierarchical Latent Constraint-Projected Diffusion Framework for Deformable Linear Objects Manipulation in Cluttered Environments","authors":"Yixiong Du;Lu Liu;Zhongkai Zhang","doi":"10.1109/LRA.2026.3699256","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699256","url":null,"abstract":"Manipulating Deformable Linear Objects (DLOs) in cluttered environments is challenging due to their high dimensionality and complex constraints. In this paper, we propose a Hierarchical Constraint-projected Diffusion (HCDiff) framework that decomposes the problem into global constraint-aware planning and local robust control through a coarse-to-fine generative process. At the high level, we propose a Latent Control Barrier Function (LCBF) projection that is integrated directly into the score-based denoising process of a diffusion planner. By projecting the gradient flow within the latent manifold learned by a Graph AutoEncoder (GAE), our method biases the generation of subgoals toward collision-free and physically feasible configurations. At the low level, we develop a Goal-conditioned Diffusion Policy (GDP) for DLO manipulation that is trained solely on unstructured, task-agnostic play data. By offloading local control to this task-agnostic policy, our high-level planner only requires a small scale of task-specific human demonstrations, thereby significantly reducing the overall dependency on expensive expert data. Furthermore, we employ a dual GDP control strategy to enhance control robustness. Comprehensive experiments in 2D and 3D demonstrate that HCDiff outperforms multiple baselines in terms of task success rate and planning efficiency. Crucially, the framework exhibits superior adaptability, successfully navigating environments with modified obstacle geometries beyond the training distribution.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8528-8535"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627893","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 Mole-Inspired Scratch-Digging Robot for Granular Media Traversal 一种受鼹鼠启发的颗粒介质挖掘机器人

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.2026.3693937

Jiabin Liu;Zhaofeng Liang;Haifei Zhu;Yisheng Guan;Kun Xu;Xilun Ding;Tao Zhang

引用次数: 0

Simulation of Adaptive Running With Flexible Sports Prosthesis Using Reinforcement Learning of Hybrid-Link System 基于Hybrid-Link系统强化学习的柔性运动假肢自适应跑步仿真

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.2026.3693933

Yuta Shimane;Ko Yamamoto

{"title":"Simulation of Adaptive Running With Flexible Sports Prosthesis Using Reinforcement Learning of Hybrid-Link System","authors":"Yuta Shimane;Ko Yamamoto","doi":"10.1109/LRA.2026.3693933","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693933","url":null,"abstract":"This study proposes a reinforcement learning–based framework for adaptive running motion simulation in a unilateral transtibial amputee using a hybrid-link system that incorporates the flexibility of a leaf-spring-type sports prosthesis. The design and selection of sports prostheses typically rely on trial and error. A comprehensive whole-body dynamics analysis that accounts for interactions between human motion and prosthetic deformation can provide valuable insights for user-specific design and selection. The proposed hybrid-link system enables such analysis by integrating a Piece-wise Constant Strain (PCS) model to represent prosthetic flexibility. Based on this system, the simulation methodology generates whole-body dynamic motions of a unilateral transtibial amputee using a reinforcement learning approach. This framework integrates imitation learning based on motion capture data with accurate computation of prosthetic dynamics. Running motions are simulated under multiple virtual prosthetic stiffness conditions, and the corresponding metabolic cost of transport (COT) obtained from these simulations is analyzed. The results suggest that variations in prosthetic stiffness influence running dynamics and performance, and that COT is consistent with values reported in prior study. Our findings demonstrate the potential of the proposed approach for simulation and analysis under virtual conditions that differ from real-world conditions.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8172-8179"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508925","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