IEEE Robotics and Automation Letters最新文献_第3页

A Virtual Mechanical Interaction Layer Enables Resilient Human-to-Robot Object Handovers 虚拟机械交互层使人与机器人之间的对象切换具有弹性

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.2026.3692046

Omar Faris;Sławomir K. Tadeja;Fulvio Forni

{"title":"A Virtual Mechanical Interaction Layer Enables Resilient Human-to-Robot Object Handovers","authors":"Omar Faris;Sławomir K. Tadeja;Fulvio Forni","doi":"10.1109/LRA.2026.3692046","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692046","url":null,"abstract":"Object handover is a common form of interaction widely used in collaborative tasks. However, achieving it efficiently remains a challenge. We address the problem of ensuring resilient robotic actions that can adapt to complex changes in object pose during human-to-robot object handovers. We propose the use of Virtual Model Control to create an interaction layer that controls the robot and adapts to the dynamic changes in the handover process. Additionally, we propose using augmented reality to facilitate bidirectional communication between humans and robots during handovers. Our controller demonstrated high resilience in experimental tests against various sources of uncertainties with a nearly perfect success rate and an average mean approach time of 4 seconds in one-time rotation or translation tests of four different objects, as well as a success rate of 100% in tests with random object motion and robot starting pose. We also performed a user study with 16 participants to understand human preferences for different robot control profiles and augmented reality visuals in object handovers. Our results showed a general preference for the proposed approach with a 98% success rate and a mean approach time of 4.9 seconds and revealed insights for improving the interaction with the user.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7780-7787"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509278","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

UniTracker: Learning Universal Whole-Body Motion Tracker for Humanoid Robots UniTracker：学习人形机器人的通用全身运动跟踪器

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-12 DOI: 10.1109/LRA.2026.3692091

Kangning Yin;Weishuai Zeng;Ke Fan;Minyue Dai;Zirui Wang;Qiang Zhang;Zheng Tian;Jingbo Wang;Jiangmiao Pang;Weinan Zhang

{"title":"UniTracker: Learning Universal Whole-Body Motion Tracker for Humanoid Robots","authors":"Kangning Yin;Weishuai Zeng;Ke Fan;Minyue Dai;Zirui Wang;Qiang Zhang;Zheng Tian;Jingbo Wang;Jiangmiao Pang;Weinan Zhang","doi":"10.1109/LRA.2026.3692091","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692091","url":null,"abstract":"Achieving generalizable whole-body motion control is essential for deploying humanoid robots in real-world environments. However, existing MLP-based policies trained under partial observations often suffer from limited expressiveness and struggle to maintain global consistency. These shortcomings manifest as less expressive motion, orientation drift, and poor generalization across diverse behaviors. To address these limitations, we propose UniTracker, a three-stage framework for scalable and adaptive motion tracking. The first stage learns a privileged teacher policy that produces high-fidelity reference actions. Building on this, the second stage trains a CVAE-based universal policy that captures a global latent representation of motion, enabling robust performance under partial observations. Crucially, we align the partial-observation prior with a full-observation encoder, injecting global intent into the latent space. In the final stage, a lightweight adaptation module fine-tunes the student policy on challenging sequences, supporting both per-instance and batch adaptation. We validate UniTracker in simulation and on a Unitree G1 humanoid robot, demonstrating superior tracking accuracy, motion diversity, and deployment robustness compared to current baselines.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8124-8131"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510005","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

Probabilistic Collision Risk Estimation Through Gauss-Legendre Cubature and Non-Homogeneous Poisson Processes 基于高斯-勒让德模型和非齐次泊松过程的概率碰撞风险估计

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.2026.3692036

Trent Weiss;Madhur Behl

引用次数: 0

PLATO Hand: Shaping Contact Behavior With Fingernails for Precise Manipulation 柏拉图手：塑造接触行为与指甲的精确操作

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.2026.3699243

Dong Ho Kang;Aaron Kim;Mingyo Seo;Kazuto Yokoyama;Tetsuya Narita;Luis Sentis

引用次数: 0

Machine Learning-Enabled Soft Finger Grasping With a Kinesthetic Sense of Touch 具有动觉触觉的机器学习软手指抓握

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-20 DOI: 10.1109/LRA.2026.3695331

Stephanie O. Herrera;Tae Myung Huh;Dejan Milutinović

引用次数: 0

ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation ORION：合作多智能体在线导航的选项正则化深度强化学习

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.2026.3699158

Shizhe Zhang;Jingsong Liang;Zhitao Zhou;Shuhan Ye;Yizhuo Wang;Derek Ming Siang Tan;Jimmy Chiun;Yuhong Cao;Guillaume Sartoretti

{"title":"ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation","authors":"Shizhe Zhang;Jingsong Liang;Zhitao Zhou;Shuhan Ye;Yizhuo Wang;Derek Ming Siang Tan;Jimmy Chiun;Yuhong Cao;Guillaume Sartoretti","doi":"10.1109/LRA.2026.3699158","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699158","url":null,"abstract":"Existing methods for multi-agent navigation typically assume fully known environments, offering limited support for partially known scenarios with outdated or imperfect prior maps, such as warehouses or factory floors. There, agents need to balance path optimality with collecting and sharing environmental information to help teammates reach their own targets. To these ends, we propose <italic>ORION</i>, a novel deep reinforcement learning framework for cooperative multi-agent online navigation in partially known environments. Starting from an imperfect prior map, ORION trains agents to make decentralized decisions, coordinate toward individual targets, and actively reduce task-relevant map uncertainty through online observation sharing in a closed perception—action loop. We first design a shared graph encoder that fuses prior map with online perception into a unified representation, providing robust state embeddings under environmental discrepancies. At the core of ORION is an option-critic framework that learns high-level cooperative modes translated into sequences of low-level actions, enabling adaptive switching between individual navigation and team-level exploration. We further introduce a dual-stage cooperation strategy that allows agents to assist teammates under map uncertainty, thereby reducing the overall makespan. Across extensive maze-like maps and large-scale warehouse environments, ORION achieves high-quality real-time decentralized cooperation while scaling to up to 10 robots, outperforming state-of-the-art classical and learning-based baselines. Finally, we validate ORION on physical robot teams, demonstrating its robustness and practicality for real-world cooperative navigation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"9016-9023"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442548","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

IEEE Robotics and Automation Letters Information for Authors IEEE机器人与自动化作者信函信息

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-26 DOI: 10.1109/LRA.2026.3705169

引用次数: 0

DIRA: Diffusion-Based Imitation-to-Reinforcement Adaptation for Task Automation of Surgical Robots 基于扩散的手术机器人任务自动化模仿-强化适应

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.2026.3699185

Guowei Wang;Xinan Sun;Yuan Xing;Rui Cao;Zhikang Ma;Xuan Zhang

{"title":"DIRA: Diffusion-Based Imitation-to-Reinforcement Adaptation for Task Automation of Surgical Robots","authors":"Guowei Wang;Xinan Sun;Yuan Xing;Rui Cao;Zhikang Ma;Xuan Zhang","doi":"10.1109/LRA.2026.3699185","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699185","url":null,"abstract":"The autonomy of surgical robots can enhance the safety and efficiency of surgeries. However, the diverse surgical skill expressions in expert demonstrations create multimodal patterns that can lead to suboptimal outcomes under unimodal policy learning methods. Additionally, there can be distribution shifts due to imbalances between positive and negative samples in surgical task data, and the learned policies may fail when encountering unseen states or external perturbations. To address this, we propose Diffusion-Based Imitation-to-Reinforcement Adaptation (DIRA), a three-stage framework that leverages a conditional diffusion-based implicit policy for multimodal action modeling, a value-consistent critic warm-up for stable value estimation, and an online strategic perturbation adaptation for smooth imitation-to-reinforcement learning transition. Experiments on the SurRoL platform show that the proposed DIRA achieves a higher success rate than existing methods, with an average improvement of 24 percentage points and up to 39 percentage points on complex tasks. Moreover, the experiments with Real-World Demonstrations further demonstrate the potential of DIRA for real-world surgical task automation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8960-8967"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442997","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

Informative Planning With Attention-Based Hybrid Belief Reinforcement Learning for Aerial Multi-Target Search and Tracking 基于注意力的混合信念强化学习的信息规划空中多目标搜索与跟踪

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-04 DOI: 10.1109/LRA.2026.3700661

Zhengyu Hua;Yike Wu;Yuwei Li;Li Xing;Jidong Huang;Peng Li;Wencan Lu;Haoyao Chen

{"title":"Informative Planning With Attention-Based Hybrid Belief Reinforcement Learning for Aerial Multi-Target Search and Tracking","authors":"Zhengyu Hua;Yike Wu;Yuwei Li;Li Xing;Jidong Huang;Peng Li;Wencan Lu;Haoyao Chen","doi":"10.1109/LRA.2026.3700661","DOIUrl":"https://doi.org/10.1109/LRA.2026.3700661","url":null,"abstract":"Autonomous multi-target search and tracking (SaT) under limited sensing fields of view requires balancing the maintenance of precise estimates for visible targets with the recovery of those lost to intermittent observations. To address this, we present <bold>HyBE-RL</b>, an integrated active perception framework that synergizes a hybrid belief estimator (<italic>HyBE</i>) with an attention-driven reinforcement learning planner. The estimation layer employs a visibility-dependent mechanism to leverage negative information from non-detections, coupling parametric tracking with non-parametric search to maintain effective priors during observation gaps. Building on this representation, the planner utilizes a spatial-uncertainty attention mechanism to map continuous belief directly to optimal control actions, explicitly overcoming the discretization artifacts and heuristic sub-optimality inherent in conventional geometric planning. To facilitate robust learning, we incorporate a sampling-based geometric planner to provide expert-guided reward shaping and function as a runtime safety shield. Comprehensive simulations and real-world uncrewed aerial vehicle (UAV) experiments validate that the proposed approach not only outperforms standard baselines but also surpasses its geometric teacher in complex environments, achieving reduced tracking errors and superior target recovery.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8936-8943"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443023","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

Structure-Aware Diffusion Policy for Bimanual Cooperative Manipulation 基于结构感知的双手协同操作扩散策略

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.2026.3701562

Xiao Li;Jie Zhang;Lequn Fu;Leyuan Gu;Youjun Xiong;Shiqi Li

{"title":"Structure-Aware Diffusion Policy for Bimanual Cooperative Manipulation","authors":"Xiao Li;Jie Zhang;Lequn Fu;Leyuan Gu;Youjun Xiong;Shiqi Li","doi":"10.1109/LRA.2026.3701562","DOIUrl":"https://doi.org/10.1109/LRA.2026.3701562","url":null,"abstract":"Bimanual cooperative manipulation involves critical closed-chain contact phases in which task success depends on maintaining inter-arm geometric consistency while mitigating antagonistic interaction. We present a structure-aware diffusion policy that conditions action generation on an explicit cooperative representation, rather than inferring coordination implicitly from raw multimodal inputs. The representation combines (i) Cooperative Dual Task-Space (CDTS) descriptors computed from forward kinematics, which decompose bimanual geometry into absolute motion and relative configuration, and (ii) a gravity-compensated force-difference cue computed from left–right end-effector forces to capture interaction inconsistency. To integrate perception with these structured priors, we design a hierarchical fusion module in which bidirectional vision–force cross-attention first aligns cross-modal evidence, followed by CDTS-guided cross-attention that uses structural tokens as queries to extract coordination-relevant features. Real-robot experiments on dual-arm cooperative carrying across objects of varying sizes and appearances demonstrate improved closed-chain stability and more robust performance across the evaluated object variations, increasing the overall success rate from 58% to 89% compared with a vision-dominant diffusion baseline.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8809-8816"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626521","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