IEEE Robotics and Automation Letters最新文献_第5页

Event-Driven Force Measurement of a Variable-Stiffness Robotic Finger Using Masked Autoencoder Pre-Training 基于掩模自编码器预训练的变刚度机械手指事件驱动力测量

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3562793

Qianyu Guo;Yawen Lu;Jiaming Fu;Dongming Gan

{"title":"Event-Driven Force Measurement of a Variable-Stiffness Robotic Finger Using Masked Autoencoder Pre-Training","authors":"Qianyu Guo;Yawen Lu;Jiaming Fu;Dongming Gan","doi":"10.1109/LRA.2025.3562793","DOIUrl":"https://doi.org/10.1109/LRA.2025.3562793","url":null,"abstract":"Force feedback in compliant robotic grippers is essential for precise manipulation tasks but remains challenging because irregular deformation of soft materials renders traditional sensor integration impractical. Event camera has emerged as a powerful alternative to conventional vision sensors through their ability to capture only temporal changes in a scene, thereby significantly reducing data redundancy and processing overhead. In this letter, we introduce a novel vision-based force prediction framework that employs a Mamba-Like architecture to process event camera data in compliant grippers. To validate our approach, we have developed a custom gripper with variable stiffness and created a comprehensive dataset comprising over <italic>9,000</i> event frames. Our methodology combines self-supervised pre-training for learning rich feature representations with a Mamba-like regression framework to achieve accurate force prediction. The proposed method demonstrates a <italic>0.14</i> improvement in RMSE when compared to existing Vision Transformer approaches. Through extensive experimental validation—including real-time performance analysis, ablation studies, and generalization tests across various gripper configurations—we demonstrate the framework's effectiveness. Our results indicate robust performance suitable for practical industrial applications, suggesting potential extensions to other compliant robotics applications that require precise force estimation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5831-5838"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902627","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

Physics-Informed Neural Networks With Unscented Kalman Filter for Sensorless Joint Torque Estimation in Humanoid Robots 基于无气味卡尔曼滤波的物理信息神经网络在人形机器人无传感器关节力矩估计中的应用

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3562792

Ines Sorrentino;Giulio Romualdi;Lorenzo Moretti;Silvio Traversaro;Daniele Pucci

{"title":"Physics-Informed Neural Networks With Unscented Kalman Filter for Sensorless Joint Torque Estimation in Humanoid Robots","authors":"Ines Sorrentino;Giulio Romualdi;Lorenzo Moretti;Silvio Traversaro;Daniele Pucci","doi":"10.1109/LRA.2025.3562792","DOIUrl":"https://doi.org/10.1109/LRA.2025.3562792","url":null,"abstract":"This paper presents a novel framework for whole-body torque control of humanoid robots without joint torque sensors, designed for systems with electric motors and high-ratio harmonic drives. The approach integrates Physics-Informed Neural Networks (PINNs) for friction modeling and Unscented Kalman Filtering (UKF) for joint torque estimation, within a real-time torque control architecture. PINNs estimate nonlinear static and dynamic friction from joint and motor velocity readings, capturing effects like motor actuation without joint movement. The UKF utilizes PINN-based friction estimates as direct measurement inputs, improving torque estimation robustness. Experimental validation on the ergoCub humanoid robot demonstrates improved torque tracking accuracy, enhanced energy efficiency, and superior disturbance rejection compared to the state-of-the-art Recursive Newton-Euler Algorithm (RNEA), using a dynamic balancing experiment. The framework's scalability is shown by consistent performance across robots with similar hardware but different friction characteristics, without re-identification. Furthermore, a comparative analysis with position control highlights the advantages of the proposed torque control approach. The results establish the method as a scalable and practical solution for sensorless torque control in humanoid robots, ensuring torque tracking, adaptability, and stability in dynamic environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5705-5712"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883372","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

Spline-FRIDA: Towards Diverse, Humanlike Robot Painting Styles With a Sample-Efficient, Differentiable Brush Stroke Model 样条frida：向多样化，人形机器人绘画风格与样本效率，可微分的笔触模型

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3563131

Lawrence Chen;Peter Schaldenbrand;Tanmay Shankar;Lia Coleman;Jean Oh

引用次数: 0

Emperror: A Flexible Generative Perception Error Model for Probing Self-Driving Planners 帝侯：用于探测自动驾驶计划者的柔性生成感知误差模型

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3562789

Niklas Hanselmann;Simon Doll;Marius Cordts;Hendrik P.A. Lensch;Andreas Geiger

{"title":"Emperror: A Flexible Generative Perception Error Model for Probing Self-Driving Planners","authors":"Niklas Hanselmann;Simon Doll;Marius Cordts;Hendrik P.A. Lensch;Andreas Geiger","doi":"10.1109/LRA.2025.3562789","DOIUrl":"https://doi.org/10.1109/LRA.2025.3562789","url":null,"abstract":"To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typically assume a setting in which the ground-truth world state is available as input. However, when deployed, planning needs to be robust to the long-tail of errors incurred by a noisy perception system, which is often neglected in evaluation. To address this, previous work has proposed drawing adversarial samples from a perception error model (PEM) mimicking the noise characteristics of a target object detector. However, these methods use simple PEMs that fail to accurately capture all failure modes of detection. In this letter, we present <sc>Emperror</small>, a novel transformer-based generative PEM, apply it to stress-test an imitation learning (IL)-based planner and show that it imitates modern detectors more faithfully than previous work. Furthermore, it is able to produce realistic noisy inputs that increase the planner's collision rate by up to 85%, demonstrating its utility as a valuable tool for a more complete evaluation of self-driving planners.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5807-5814"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902626","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

Dynamic Electromagnetic Navigation 动态电磁导航

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3563130

Jasan Zughaibi;Bradley J. Nelson;Michael Muehlebach

{"title":"Dynamic Electromagnetic Navigation","authors":"Jasan Zughaibi;Bradley J. Nelson;Michael Muehlebach","doi":"10.1109/LRA.2025.3563130","DOIUrl":"https://doi.org/10.1109/LRA.2025.3563130","url":null,"abstract":"Magnetic navigation offers wireless control over magnetic objects, which has important medical applications, such as targeted drug delivery and minimally invasive surgery. Magnetic navigation systems are categorized into systems using permanent magnets and systems based on electromagnets. Electromagnetic Navigation Systems (eMNSs) are believed to have a superior actuation bandwidth, facilitating trajectory tracking and disturbance rejection. This greatly expands the range of potential medical applications and includes even dynamic environments as encountered in cardiovascular interventions. To showcase the dynamic capabilities of eMNSs, we successfully stabilize a (non-magnetic) inverted pendulum on the tip of a magnetically driven arm. Our approach employs a model-based framework that leverages Lagrangian mechanics to capture the interaction between the mechanical dynamics and the magnetic field. Using system identification, we estimate unknown parameters, the actuation bandwidth, and characterize the system's nonlinearity. To explore the limits of electromagnetic navigation and evaluate its scalability, we characterize the electrical system dynamics and perform reference measurements on a clinical-scale eMNS, affirming that the proposed dynamic control methodologies effectively translate to larger coil configurations. A state-feedback controller stabilizes the inherently unstable pendulum, and an iterative learning control scheme enables accurate tracking of non-equilibrium trajectories. Furthermore, to understand structural limitations of our control strategy, we analyze the influence of magnetic field gradients on the motion of the system. To our knowledge, this is the first demonstration to stabilize a 3D inverted pendulum through electromagnetic navigation.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6095-6102"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908389","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

Resilient Online Planning for Mobile Robots With Minimal Relaxation of Signal Temporal Logic Specifications 具有最小信号时间逻辑规范松弛的移动机器人弹性在线规划

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3563119

Ali Tevfik Buyukkocak;Derya Aksaray

引用次数: 0

Stereo Hand-Object Reconstruction for Human-to-Robot Handover 面向人机切换的立体手-物重建

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3562790

Yik Lung Pang;Alessio Xompero;Changjae Oh;Andrea Cavallaro

引用次数: 0

Enhancing Dual-Loop Pressure Control in Pneumatic Soft Robotics With a Comparison of Evolutionary Algorithms for PID & FOPID Controller Tuning 改进气动软机器人双环压力控制：PID与FOPID控制器整定的进化算法比较

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-21 DOI: 10.1109/LRA.2025.3563163

Mostafa Mo. Massoud;Paulo H.T.F Alves;Jacqueline Libby

{"title":"Enhancing Dual-Loop Pressure Control in Pneumatic Soft Robotics With a Comparison of Evolutionary Algorithms for PID & FOPID Controller Tuning","authors":"Mostafa Mo. Massoud;Paulo H.T.F Alves;Jacqueline Libby","doi":"10.1109/LRA.2025.3563163","DOIUrl":"https://doi.org/10.1109/LRA.2025.3563163","url":null,"abstract":"The control of pneumatic soft robotics is challenging due to nonlinearites arising from many factors including pneumatic system components and material properties of the soft actuator. Manual methods for PID controller tuning are inadequate for the nonlinear and time-variant dynamics present in soft robotics. Affordable pneumatic components such as on/off valves cause discontinuities in flow rate, introducing nonlinearities and oscillatory fluctuations into the system. This study proposes a dual-loop control system: one for PID and Fractional-Order PID (FOPID) control of a solenoid valve that feeds air into the actuator, and another for PID control of the pump upstream of the valve. The PID and FOPD parameters are optimized using evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). Simulations and real-world experiments are conducted to validate the optimized parameters. Our results demonstrate that the dual-loop hardware configuration reduces fluctuations from the valves compared with a single-loop control scheme. The experimental statistical analysis confirms that FOPID achieves the highest significant improvements in rise time (PSO) and peak time (GA, PSO), while PID performs better for overshoot (GA, PSO). These findings highlight the importance of selecting an appropriate optimization algorithm based on the specific control objective, as FOPID does not outperform PID in every metric across all methods.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"6119-6126"},"PeriodicalIF":4.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10972052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tree-Based Grafting Approach for Bidirectional Motion Planning With Local Subsets Optimization 基于树的局部子集优化双向运动规划方法

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-18 DOI: 10.1109/LRA.2025.3562369

Liding Zhang;Yao Ling;Zhenshan Bing;Fan Wu;Sami Haddadin;Alois Knoll

{"title":"Tree-Based Grafting Approach for Bidirectional Motion Planning With Local Subsets Optimization","authors":"Liding Zhang;Yao Ling;Zhenshan Bing;Fan Wu;Sami Haddadin;Alois Knoll","doi":"10.1109/LRA.2025.3562369","DOIUrl":"https://doi.org/10.1109/LRA.2025.3562369","url":null,"abstract":"Bidirectional motion planning often reduces planning time compared to its unidirectional counterparts. It requires connecting the forward and reverse search trees to form a continuous path. However, this process could fail and restart the asymmetric bidirectional search due to the limitations of lazy-reverse search. To address this challenge, we propose Greedy GuILD Grafting Trees (G3T*), a novel path planner that grafts invalid edge connections at both ends to re-establish tree-based connectivity, enabling rapid path convergence. G3T* employs a greedy approach using the minimum Lebesgue measure of guided incremental local densification (GuILD) subsets to optimize paths efficiently. Furthermore, G3T* dynamically adjusts the sampling distribution between the informed set and GuILD subsets based on historical and current cost improvements, ensuring asymptotic optimality. These features enhance the forward search's growth towards the reverse tree, achieving faster convergence and lower solution costs. Benchmark experiments across dimensions from <inline-formula><tex-math>$mathbb {R}^{2}$</tex-math></inline-formula> to <inline-formula><tex-math>$mathbb {R}^{8}$</tex-math></inline-formula> and real-world robotic evaluations demonstrate G3T*’s superior performance compared to existing single-query sampling-based planners.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 6","pages":"5815-5822"},"PeriodicalIF":4.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLM-Based Multi-Agent Decision-Making: Challenges and Future Directions 基于llm的多智能体决策：挑战与未来方向

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-04-18 DOI: 10.1109/LRA.2025.3562371

Chuanneng Sun;Songjun Huang;Dario Pompili

引用次数: 0