IEEE Robotics and Automation Letters最新文献_第4页

Design of a Cable-Driven, Self-Aligning Knee Exoskeleton to Eliminate Joints’ Misalignment Across Users With Varying Leg Dimensions 一种电缆驱动的自对准膝关节外骨骼的设计，以消除不同腿部尺寸用户的关节错位

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.2026.3699123

Xinhua Yang;Sheng Guo;Lianzheng Niu;Duxin Liu;Majun Song;Peiyi Wang;Xiangyang Wang

{"title":"Design of a Cable-Driven, Self-Aligning Knee Exoskeleton to Eliminate Joints’ Misalignment Across Users With Varying Leg Dimensions","authors":"Xinhua Yang;Sheng Guo;Lianzheng Niu;Duxin Liu;Majun Song;Peiyi Wang;Xiangyang Wang","doi":"10.1109/LRA.2026.3699123","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699123","url":null,"abstract":"One of the primary difficulties in exoskeleton use is the misalignment between the device’s mechanical joints and the user’s natural biological joints. Knee exoskeletons, widely studied for assisting walking and stand-to-sit transitions, typically require precise adjustment of the rigid frame to match the user’s knee joint axis. If not properly aligned, parasitic forces arise at the physical human–robot interface (pHRI), causing significant discomfort. To address this problem, we propose a cable-driven, self-aligning knee exoskeleton that accommodates different wearing positions without the need for accurate pre-use alignment. Platform experiments showed that the exoskeleton adapted well to variations in wearing position in the deactivated mode and transmitted assistive torque with limited changes in parasitic forces when activated. At an assistive torque of 18 Nm, the relative error between the measured and target values was 7.27%. Human-subject experiments further demonstrated substantial reductions in relative sliding distance (RSD) and parasitic forces during activities such as walking and stand-to-sit transitions: the maximum RSD was reduced by 84.9% compared with the Orthosis condition and by 85.1% compared with the Locked condition; the maximum parasitic force was reduced by 89.1% and 89.0%, respectively, and the maximum parasitic torque was reduced by 95.6% and 96.1%, respectively.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8592-8599"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627574","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

PGDP: Physics-Guided Diffusion Policy for Multi-Rate Control in Contact-Rich Manipulation PGDP：多速率控制的物理指导扩散策略在接触丰富的操作

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/LRA.2026.3701123

Deliang Zhu;Yue Li;Yang Li;Shijie Guo

{"title":"PGDP: Physics-Guided Diffusion Policy for Multi-Rate Control in Contact-Rich Manipulation","authors":"Deliang Zhu;Yue Li;Yang Li;Shijie Guo","doi":"10.1109/LRA.2026.3701123","DOIUrl":"https://doi.org/10.1109/LRA.2026.3701123","url":null,"abstract":"In contact-rich manipulation, coarse-timescale action generation alone is often insufficient to maintain stable interaction when contact conditions change rapidly during execution. As a result, learned policies may struggle to suppress force spikes, incipient slip, and object or tissue deformation online, limiting robustness in high-contact tasks. To address this issue, we present Physics-Guided Diffusion Policy (PGDP), a visuo-tactile manipulation framework that combines diffusion-based motion intent generation with high-frequency tactile-constrained refinement. We develop a customizable three-dimensional curved-surface tactile sensor and a Tactile Manifold Prior Encoder (TMPE) to capture temporally structured tactile representations and provide physically informative visuo-tactile conditions for policy learning. At the motion-generation level, a low-frequency Conditional Diffusion Model (CDM) predicts short-horizon Cartesian action intents from visual and tactile context. At execution time, a high-frequency Physics-Guided Module (PGM) solves a constrained quadratic program in the joint space to track the diffusion intent while enforcing tactile-driven safety and contact-consistency requirements. Experiments on block-uprighting and massage tasks show that PGDP increases the unitless composite score from 0.80 to 0.87 and from 0.77 to 0.89, respectively, compared with the strongest two-rate decoder baseline.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8833-8840"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148628073","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 Compliant Force Control Strategy for On-Orbit Assembly: Design, Implementation, and Reinforcement Learning-Based Optimization 在轨装配柔性力控制策略：设计、实现和基于强化学习的优化

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.2026.3699242

Xinle Yan;Lingling Shi;Yong Hu;Yanan Liu;Minghe Shan

{"title":"A Compliant Force Control Strategy for On-Orbit Assembly: Design, Implementation, and Reinforcement Learning-Based Optimization","authors":"Xinle Yan;Lingling Shi;Yong Hu;Yanan Liu;Minghe Shan","doi":"10.1109/LRA.2026.3699242","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699242","url":null,"abstract":"On-orbit assembly of large-scale space structures is critical for future space missions, yet it poses significant challenges for robotic systems. A fundamental difficulty in tasks like docking, which are highly contact-rich, lies in achieving both high precision and compliant force control to prevent damage. This trade-off between accuracy and safety is difficult to resolve using conventional methods. Furthermore, the development of advanced solutions is constrained by the scarcity of high-fidelity ground testbeds capable of emulating the complex contact dynamics of walking and docking. To address these issues, this letter presents a comprehensive system-level solution. First, a novel seven-degree-of-freedom walking and assembly integrated robotic platform, along with a high-load-bearing docking interface, is developed to facilitate practical verification. Second, based on this platform, a novel hybrid compliant control framework is proposed, which integrates a low-level impedance controller with a high-level Model-Based Reinforcement Learning (MBRL) planner. Unlike passive compliance schemes, the MBRL agent leverages a learned dynamics model to predict interaction forces and proactively generate corrective force trajectory commands. This hierarchical architecture allows the system to accommodate geometric uncertainties and maintain continuous contact without triggering excessive force spikes. Experimental results validate the effectiveness of the proposed approach. Compared to traditional impedance control, the hybrid control strategy significantly reduces contact forces during docking while achieving a final high assembly accuracy. This work demonstrates a viable system-level solution, from hardware design to intelligent control, for advancing robotic on-orbit assembly.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8624-8631"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148628158","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

Corridor-Driven Topological Planning With Nonlinear MPC for Agile Quadrotor Flight 基于非线性MPC的敏捷四旋翼飞行廊道驱动拓扑规划

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.2026.3700428

Honghao Pan;Hang Wang;Farshad Arvin;Junyan Hu

引用次数: 0

SI-Diff: A Framework for Learning Search and High-Precision Insertion With a Force-Domain Diffusion Policy 基于力域扩散策略的学习搜索和高精度插入框架

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-15 DOI: 10.1109/LRA.2026.3693991

Yibo Liu;Stanko Oparnica;Simon Shewchun-Jakaitis;Guoyi Fu;Jie Wang;Jun Yang;Anand Jagannathan;Tony Hong-Yau Lo

{"title":"SI-Diff: A Framework for Learning Search and High-Precision Insertion With a Force-Domain Diffusion Policy","authors":"Yibo Liu;Stanko Oparnica;Simon Shewchun-Jakaitis;Guoyi Fu;Jie Wang;Jun Yang;Anand Jagannathan;Tony Hong-Yau Lo","doi":"10.1109/LRA.2026.3693991","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693991","url":null,"abstract":"Contact-rich assembly is fundamental in robotics but poses significant challenges due to uncertainties in relative poses, such as misalignments and small clearances in peg-in-hole tasks. Existing approaches typically address search and high-precision insertion separately, because these tasks involve distinct action patterns. However, supporting both tasks within a single model, without switching models or weights, is desirable for intelligent assembly systems. In this work, we propose SI-Diff, a framework that learns both search and high-precision insertion through a force-domain diffusion policy. To this end, we introduce a new mode-conditioning mechanism that enables the policy to capture distinct action behaviors under a single framework. Moreover, we develop a new search teacher policy that can generate diverse trajectories. By training on successful and efficient demonstrations provided by the teacher policy, the model learns the mapping from tactile and end-effector velocity observations to effective action behaviors. We conduct thorough experiments to show that SI-Diff extends the tolerance to x–y misalignments from 2 mm to 5 mm compared to the state-of-the-art baseline, TacDiffusion [1], while also demonstrating strong zero-shot transferability to unseen shapes.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8244-8251"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504469","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

Robust Dual-Modal Grasping Using a Stiffness-Switchable Adhesive Array 基于刚度可切换黏着阵列的鲁棒双峰抓取

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-14 DOI: 10.1109/LRA.2026.3693582

Lvzhou Li;Kai Zhu;Qingyue Li;Xiangli Wen;Xu Dong;Yaoyao Jiang;Jianning Ding

{"title":"Robust Dual-Modal Grasping Using a Stiffness-Switchable Adhesive Array","authors":"Lvzhou Li;Kai Zhu;Qingyue Li;Xiangli Wen;Xu Dong;Yaoyao Jiang;Jianning Ding","doi":"10.1109/LRA.2026.3693582","DOIUrl":"https://doi.org/10.1109/LRA.2026.3693582","url":null,"abstract":"This letter presents a dual-modal adaptive gripper (DMAG) that addresses the persistent trilemma in robotic grasping among shape adaptability, high load capacity, and environmental resilience. By introducing a temporally decoupled “conform-then-lock” paradigm, the DMAG separates geometric conformation from force transmission through a fully mechanical, media-agnostic locking mechanism. Inspired by the stiffness modulation of anemones, it translates this biological principle into a fully mechanical architecture, enabling a modular adhesive array to switch between compliant and rigid states. Experimental validation confirms: (1) a 111-fold stiffness modulation achieving over 100% grasp force enhancement; (2) sustained functionality from atmospheric pressure to low-vacuum (<inline-formula><tex-math>$1.3times 10^{2}$</tex-math></inline-formula> Pa); (3) <inline-formula><tex-math>$>95%$</tex-math></inline-formula> grasp success rate under 20% simulated unit failure; and (4) reliable grasping across two orders of magnitude in object scale. The DMAG establishes an integrated hardware architecture that reconciles the complementary strengths of soft, articulated, and adhesive grippers while overcoming their inherent limitations.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8164-8171"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508007","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

HALO: Language-Conditioned Overhead Monocular Aerial Exploration and Navigation HALO：有语言条件的头顶单目空中探测和导航

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-03-11 DOI: 10.1109/LRA.2026.3692052

Yuezhan Tao;Dexter Ong;Fernando Cladera;Jason Hughes;Camillo J. Taylor;Pratik Chaudhari;Vijay Kumar

{"title":"HALO: Language-Conditioned Overhead Monocular Aerial Exploration and Navigation","authors":"Yuezhan Tao;Dexter Ong;Fernando Cladera;Jason Hughes;Camillo J. Taylor;Pratik Chaudhari;Vijay Kumar","doi":"10.1109/LRA.2026.3692052","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692052","url":null,"abstract":"We demonstrate real-time overhead aerial metric-semantic mapping and exploration using a monocular camera paired with a global positioning system (GPS). Our system, named HALO, addresses two key challenges: (i) real-time dense 3D reconstruction using vision at large distances, and (ii) mapping and exploration of large-scale outdoor environments with accurate scene geometry and semantics. We demonstrate that HALO can plan informative paths that exploit this information to complete missions with multiple tasks specified in natural language. In simulation-based evaluation across large-scale environments of size up to 78,000 sq. m., HALO consistently completes tasks with less exploration time and achieves up to 68% higher competitive ratio in terms of the distance traveled compared to the state-of-the-art semantic exploration baseline. We use real-world experiments on a custom quadrotor platform to demonstrate that (i) all modules can run onboard the robot, and that (ii) in diverse environments HALO can support effective autonomous execution of missions covering up to 24,600 sq. m. area at an altitude of 40 m.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8204-8211"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508793","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 Society Publication Information IEEE机器人与自动化协会出版信息

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.2026.3705160

引用次数: 0

ARTI-VIO: Asynchronous Multi-Camera Visual-Inertial Odometry With Feature Recall and Time-Interpolated Optimization artii - vio：异步多相机视觉惯性里程计与特征召回和时间插值优化

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.2026.3692048

Yu Feng;Fanzhe Kong;Fuyong Wang

{"title":"ARTI-VIO: Asynchronous Multi-Camera Visual-Inertial Odometry With Feature Recall and Time-Interpolated Optimization","authors":"Yu Feng;Fanzhe Kong;Fuyong Wang","doi":"10.1109/LRA.2026.3692048","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692048","url":null,"abstract":"Multi-camera visual-inertial odometry (VIO) provides robust state estimation across wide fields of view, but asynchronous sensor streams without hardware synchronization introduce algorithmic and computational challenges. We present ARTI-VIO, a flexible framework for asynchronous multi-camera VIO with self-regulating stream rate control. In the front-end, we integrate a feature recall mechanism into the standard pipeline. Instead of discarding lost features, we temporarily retain them and re-associates them with newly extracted points via descriptor matching, thereby improving inter-frame feature consistency. In the back-end, we adopt a unified time-interpolated optimization framework that models asynchronous poses as interpolated functions over a sparse set of anchor poses, enabling the direct incorporation of misaligned observations as geometric constraints in the optimization. Experiments on a custom challenging dataset demonstrate that ARTIVIO maintains real-time performance across various camera configurations and achieves competitive accuracy and robustness compared with state-of-the-art baselines.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"7764-7771"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508805","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

Condition-Number Adaptive-Weight PINN (A-PINN): A High-Fidelity and Real-Time Forward Kinematics Solver for Stewart Platforms 条件数自适应权PINN (A-PINN)：一种高保真实时Stewart平台正解算法

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.2026.3692332

Xinyu Tian;Junlin Xiao;Hang Xu;Xing Hou;Fuhua Jia;Xiaoying Yang;Salman Ijaz;Adam Rushworth;Donglei Sun

引用次数: 0