IEEE Robotics and Automation Letters最新文献_第6页

Stable Tracking-in-the-Loop Control of Cable-Driven RCM Surgical Manipulators Under Erroneous Kinematic Chains 错误运动链下缆索驱动RCM手术机械臂的稳定环内跟踪控制。

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.2026.3699136

Neelay Joglekar;Fei Liu;Florian Richter;Michael C. Yip

{"title":"Stable Tracking-in-the-Loop Control of Cable-Driven RCM Surgical Manipulators Under Erroneous Kinematic Chains","authors":"Neelay Joglekar;Fei Liu;Florian Richter;Michael C. Yip","doi":"10.1109/LRA.2026.3699136","DOIUrl":"10.1109/LRA.2026.3699136","url":null,"abstract":"Remote Center of Motion (RCM) robotic manipulators have revolutionized Minimally Invasive Surgery, enabling precise, dexterous surgical manipulation within the patient's body cavity without disturbing the insertion point on the patient. Accurate RCM tool control is vital for incorporating autonomous subtasks like suturing, blood suction, and tumor resection into robotic surgical procedures, reducing surgeon fatigue and improving patient outcomes. However, these cable-driven systems are subject to significant joint reading errors, corrupting the kinematics computation necessary to perform control. Although visual tracking with endoscopic cameras can correct errors on in-view joints, errors in the kinematic chain prior to the insertion point are irreparable because they remain out of view. No prior work has characterized the stability of control under these conditions. We fill this gap by designing a provably stable tracking-in-the-loop controller for the out-of-view portion of the RCM manipulator kinematic chain. We additionally incorporate this controller into a bilevel control scheme for the full kinematic chain. We rigorously benchmark our method in simulated and real world settings to verify our theoretical findings. Our work provides key insights into the next steps required for the transition from teleoperated to autonomous surgery.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8608-8615"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148375593","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

High-Stability Grasp Pose Estimation Method for Unknown Objects Based on Hybrid Constraint Mechanism 基于混合约束机构的未知目标高稳定抓取姿态估计方法

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.2026.3699237

Te Li;Zhixiang Yang;Haibo Liu;Tianran Liu;Xingjian Liu;Yongqing Wang

{"title":"High-Stability Grasp Pose Estimation Method for Unknown Objects Based on Hybrid Constraint Mechanism","authors":"Te Li;Zhixiang Yang;Haibo Liu;Tianran Liu;Xingjian Liu;Yongqing Wang","doi":"10.1109/LRA.2026.3699237","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699237","url":null,"abstract":"High-stability grasp pose estimation for unknown objects in real-world environments is a vital yet challenging task in visual measurement. However, most existing methods predominantly focus on grasp success rates while neglecting stability, which is a crucial factor for ensuring smooth subsequent operations. This paper proposes a high-stability grasp pose estimation method based on adaptive skeleton sampling and a hybrid constraint mechanism. Specifically, a pose generation algorithm based on adaptive skeleton extraction is first proposed. This algorithm analyzes the geometric information of object point cloud to determine grasp points and generate diverse grasp candidates without relying on a priori models. To select stable candidates, a three-stage physical stability filtering method incorporating force closure, form closure, and force balance is designed to analyze and filter out physically unstable candidates, thereby retaining high-stability alternatives, and eliminating uncertainty. Furthermore, to determine the final execution pose, a grasp pose evaluation and decision-making strategy considering stability, reachability, and safety is proposed. By integrating multiple metrics to further enhance stability and safety, an evaluation function is constructed to screen the candidates and output the optimal grasp pose. The effectiveness of the proposed method is verified through both simulation and real-world grasping experiments. Experimental results demonstrate that, compared with existing methods, the stability performance of our method is significantly improved(<inline-formula><tex-math>$>10%$</tex-math></inline-formula>)and effectively generalizes to unknown objects with varying shapes.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8632-8639"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627539","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

End-to-End Policy Learning for Hip Exoskeleton via Reinforcement Learning and Reflex-Based Musculoskeletal Simulation 基于强化学习和基于反射的肌肉骨骼模拟的髋关节外骨骼端到端策略学习

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.2026.3699246

Hossein Barati;Sangdo Kim;Nguyen Thanh Xuan;Jongwon Lee;Young Jin Park

{"title":"End-to-End Policy Learning for Hip Exoskeleton via Reinforcement Learning and Reflex-Based Musculoskeletal Simulation","authors":"Hossein Barati;Sangdo Kim;Nguyen Thanh Xuan;Jongwon Lee;Young Jin Park","doi":"10.1109/LRA.2026.3699246","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699246","url":null,"abstract":"Lower-limb exoskeletons can reduce human effort during walking; however, conventional controllers often require manual tuning and exhibit limited adaptability across users and environments. This study presents a lightweight sim-to-real reinforcement learning (RL) framework that learns continuous hip-assistive torque policies entirely in simulation and transfers them directly to a wearable hip exoskeleton. A sequence-aware long short-term memory proximal policy optimization (LSTM-PPO) controller is proposed to improve a standard multilayer perceptron PPO (MLP-PPO) baseline. A reflex-driven planar musculoskeletal model is used to generate physiologically realistic gait, while training incorporates domain randomization and sensor-noise injection to enhance robustness. The learned policies were deployed directly on a lightweight hip exoskeleton and evaluated with eight healthy participants on a treadmill, with outdoor walking assessed in a single-participant proof-of-concept. Both controllers reduced net metabolic cost relative to unassisted walking (15.2% for LSTM-PPO and 11.0% for MLP-PPO). The LSTM-based controller produced smoother assistance and demonstrated improved adaptation to variations in speed and terrain. These results indicate that reflex-based musculoskeletal simulation, combined with sequence-aware RL, can yield efficient and transferable assistance, supporting scalable and user-adaptive hip-exoskeleton control.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8672-8679"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148628091","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 Rigid Body Pose Estimation for Sparse Point Sets Under Heteroscedastic Noise 异方差噪声下稀疏点集刚体姿态的鲁棒估计

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/LRA.2026.3701525

Hao Wang;Taogang Hou;Tianmiao Wang;Buhui Jiang

{"title":"Robust Rigid Body Pose Estimation for Sparse Point Sets Under Heteroscedastic Noise","authors":"Hao Wang;Taogang Hou;Tianmiao Wang;Buhui Jiang","doi":"10.1109/LRA.2026.3701525","DOIUrl":"https://doi.org/10.1109/LRA.2026.3701525","url":null,"abstract":"Rigid body pose estimation from sparse point correspondences is important in robotics but remains challenging when measurements are affected by heteroscedastic noise and occasional outliers. This issue is particularly evident in marker-based tracking of fast-moving platforms, where localization uncertainty may vary substantially across markers due to motion blur, occlusion, and sensing artifacts. To address this setting, we develop a robust registration framework tailored to sparse and structured point sets by modeling residuals with a heavy-tailed Student's <italic>t</i>-distribution. Using its Gaussian scale mixture representation, the estimation problem is formulated as a maximum-likelihood objective with latent correspondence reliabilities, and solved via an iteratively reweighted least-squares procedure with a graduated non-convexity schedule. The resulting method adaptively down-weights unreliable measurements while retaining the efficiency of weighted rigid alignment. Simulation results across multiple noise models and outlier ratios show that the proposed approach achieves consistently low rotation and translation errors in sparse regimes. Real-world experiments on high-speed quadrotor tracking further demonstrate improved geometric consistency and reduced trajectory jerk relative to standard least-squares estimation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"9048-9055"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442275","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

SplatXtRact: Tractable Gaussian Splatting via Open World Region-of-Interest Extraction and Refinement SplatXtRact：基于开放世界兴趣区域提取和细化的可处理高斯飞溅

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.2026.3699257

Hannah Schieber;Constantin Kleinbeck;Angela P. Schoellig;Stefan Leutenegger;Daniel Roth

引用次数: 0

AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations 自动路径：学习可转移目标条件下的随机路径安全导航，没有人类示范

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.2026.3699267

Ziyang Zhang;Boyang Zhou;Zesong Yang;Haocheng Peng;Zeming Gai;Xiao Liang;Yujun Shen;Danping Zou;Ruizhen Hu;Hujun Bao;Zhaopeng Cui

{"title":"AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations","authors":"Ziyang Zhang;Boyang Zhou;Zesong Yang;Haocheng Peng;Zeming Gai;Xiao Liang;Yujun Shen;Danping Zou;Ruizhen Hu;Hujun Bao;Zhaopeng Cui","doi":"10.1109/LRA.2026.3699267","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699267","url":null,"abstract":"Real-time navigation in cluttered and dynamic environments requires collision-free and dynamically feasible motion under limited perception. However, feasible navigation behaviors are inherently multimodal because multiple paths may exist around obstacles. In this paper, we formulate navigation as learning a transferable goal-conditioned stochastic path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations. This formulation enables structured sampling of navigation candidates, allowing multiple feasible paths to be explored through sampling without relying on robot-specific motion constraints. To this end, we introduce a goal-aligned canonical state representation that removes in-plane rotational ambiguity and normalizes local geometry with respect to the goal, enabling rotation-invariant path distribution learning. We further develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning. Extensive experiments in dense static environments and dynamic pedestrian scenarios demonstrate that the proposed method achieves consistently high success rates with competitive efficiency while enabling cross-platform transfer of a single path prior learned on differential-drive robots to quadruped platforms without retraining.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8656-8663"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626595","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

Feedback-Guided Hierarchical Transmission for Multi-Robot Mapping Under Bandwidth Constraints 带宽约束下多机器人映射的反馈制导分层传输

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.2026.3699241

Guanchong Niu;Shuwake Nuerdaolieti;Jinglei Li;Zewei Jing;Lanxiang Hou;Jiayi Sun

{"title":"Feedback-Guided Hierarchical Transmission for Multi-Robot Mapping Under Bandwidth Constraints","authors":"Guanchong Niu;Shuwake Nuerdaolieti;Jinglei Li;Zewei Jing;Lanxiang Hou;Jiayi Sun","doi":"10.1109/LRA.2026.3699241","DOIUrl":"https://doi.org/10.1109/LRA.2026.3699241","url":null,"abstract":"As multi-robot mapping systems expand into data-intensive environments, efficient data transmission methods are essential for maintaining global consistency and safety under scarce and time-varying bandwidth. In this work, we propose a Feedback-Guided Hierarchical Transmission framework for centralized multi-robot mapping. In this framework, the submap representation is structured with three layers: L1 extracts the pose-and-factor skeleton to ensure topological consistency with minimal overhead; L2 streams geometric details, prioritized by semantic-weighted entropy reduction to refine the occupancy map; L3 delivers semantic labels to support high-level understanding. To optimize transmission, the backend performs centralized occupancy fusion and employs Truncated Least Squares Graduated Non-Convexity (TLS–GNC) pose-graph optimization and a Lambda-Field risk model to produce guidance signals. These signals dynamically reshape the utility curves of the layers to prioritize loop-closure consistency and safety-critical regions. Experiments are conducted on S3DIS and ScanNet indoor RGB-D datasets to validate the effectiveness of the proposed framework. The performance evaluation under bandwidth sweeps and packet loss tests indicates that our method reaches joint geometry-and-semantics accuracy with fewer bytes, recovers loop closures faster, and lowers estimated collision risk in rendezvous regions compared with non-hierarchical baselines.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8576-8583"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626935","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

Velocity Obstacle-Based Control Barrier Function for Safety-Critical Predictive Control 基于速度障碍的安全关键预测控制障碍函数

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.2026.3699239

Francesco Trotti;Daniele Meli

引用次数: 0

FastStair: Learning to Run up Stairs With Humanoid Robots 快速楼梯：学习与人形机器人跑上楼

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.2026.3701575

Yan Liu;Tao Yu;Haolin Song;Hongbo Zhu;Nianzong Hu;Yuzhi Hao;Xiuyong Yao;Xizhe Zang;Hua Chen;Jie Zhao

{"title":"FastStair: Learning to Run up Stairs With Humanoid Robots","authors":"Yan Liu;Tao Yu;Haolin Song;Hongbo Zhu;Nianzong Hu;Yuzhi Hao;Xiuyong Yao;Xizhe Zang;Hua Chen;Jie Zhao","doi":"10.1109/LRA.2026.3701575","DOIUrl":"https://doi.org/10.1109/LRA.2026.3701575","url":null,"abstract":"Running up stairs is effortless for humans but remains extremely challenging for humanoid robots due to the simultaneous requirements of high agility and strict stability. Model-free reinforcement learning (RL) can generate dynamic locomotion, yet implicit stability rewards and heavy reliance on task-specific reward shaping tend to result in unsafe behaviors, especially on stairs; conversely, model-based foothold planners encode contact feasibility and stability structure, but enforcing their hard constraints often induces conservative motion that limits speed. We present FastStair, a planner-guided, multi-stage learning framework that reconciles these complementary strengths to achieve fast and stable stair ascent. FastStair integrates a parallel model-based foothold planner into the RL training loop to bias exploration toward dynamically feasible contacts and to pretrain a safety-focused base policy. To mitigate planner-induced conservatism and the discrepancy between low- and high-speed action distributions, the base policy was fine-tuned into speed-specialized experts. These experts are retained as separate rule-switched branches, each equipped with independent LoRA layers that are co-fine-tuned to smooth the switching transition, yielding a single deployable controller that operates reliably across the full commanded-speed range. We deploy the resulting controller on the Oli humanoid robot, achieving stable stair ascent at commanded speeds up to 1.65 m/s and traversing a 33-step spiral staircase (17 cm rise per step) in 12 s, demonstrating robust high-speed performance on long staircases.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8841-8847"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627175","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

An Orthogonal-Axis Spherical Parallel Mechanism With Large Workspace for Humanoid Hip Joints: Design, Optimization, and Experimental Validation 具有大工作空间的人形髋关节正交轴球面并联机构：设计、优化和实验验证

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.2026.3692330

Haotian Liu;Zhaoming Liu;Hongwei Wang;Long Cui

{"title":"An Orthogonal-Axis Spherical Parallel Mechanism With Large Workspace for Humanoid Hip Joints: Design, Optimization, and Experimental Validation","authors":"Haotian Liu;Zhaoming Liu;Hongwei Wang;Long Cui","doi":"10.1109/LRA.2026.3692330","DOIUrl":"https://doi.org/10.1109/LRA.2026.3692330","url":null,"abstract":"Conventional serial-chain configurations for humanoid robot lower limbs exhibit issues such as concentrated driving torques, inertial coupling, and error accumulation, leading to inadequate joint stiffness, limited control precision, and reduced dynamic stability. To overcome these drawbacks, this study proposes a novel Orthogonal-axis Spherical Parallel Mechanism, inspired by the ball-and-socket structure of the human hip joint. The kinematic model of the mechanism is established. A multi-objective optimization framework is then constructed, considering workspace coverage, dexterity, and maximum joint torque, and optimal parameters are obtained through weighted decision-making. Simulations and experiments verify that the optimized mechanism fully covers the common range of motion of the human hip joint, demonstrates improved dexterity within common motion ranges, and supports a single-leg payload of 40 kg. Integrating high stiffness, a large workspace, and superior load capability, this design offers a comprehensive solution for humanoid robotic legs.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"11 7","pages":"8268-8275"},"PeriodicalIF":5.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504464","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