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

Human-Robot Collaborative Tele-Grasping in Clutter With Five-Fingered Robotic Hands

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527278

Yayu Huang;Dongxuan Fan;Dashun Yan;Wen Qi;Guoqiang Deng;Zhihao Shao;Yongkang Luo;Daheng Li;Zhenghan Wang;Qian Liu;Peng Wang

{"title":"Human-Robot Collaborative Tele-Grasping in Clutter With Five-Fingered Robotic Hands","authors":"Yayu Huang;Dongxuan Fan;Dashun Yan;Wen Qi;Guoqiang Deng;Zhihao Shao;Yongkang Luo;Daheng Li;Zhenghan Wang;Qian Liu;Peng Wang","doi":"10.1109/LRA.2025.3527278","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527278","url":null,"abstract":"Teleoperation offers the possibility of enabling robots to replace humans in operating within hazardous environments. While it provides greater adaptability to unstructured settings than full autonomy, it also imposes significant burdens on human operators, leading to operational errors. To address this challenge, shared control, a key aspect of human-robot collaboration methods, has emerged as a promising alternative. By integrating direct teleoperation with autonomous control, shared control ensures both efficiency and stability. In this letter, we introduce a shared control framework for human-robot collaborative tele-grasping in clutter with five-fingered robotic hands. During teleoperation, the operator's intent to reach the target object is detected in real-time. Upon successful detection, continuous and smooth grasping plans are generated, allowing the robot to seamlessly take over control and achieve natural, collision-free grasping. We validate the proposed framework through fundamental component analysis and experiments on real-world platforms, demonstrating the superior performance of this framework in reducing operator workload and enabling effective grasping in clutter.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2215-2222"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106594","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

Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute 一分钟内基于学习的规模化自动驾驶赛车赛道系统辨识

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527336

Onur Dikici;Edoardo Ghignone;Cheng Hu;Nicolas Baumann;Lei Xie;Andrea Carron;Michele Magno;Matteo Corno

{"title":"Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute","authors":"Onur Dikici;Edoardo Ghignone;Cheng Hu;Nicolas Baumann;Lei Xie;Andrea Carron;Michele Magno;Matteo Corno","doi":"10.1109/LRA.2025.3527336","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527336","url":null,"abstract":"Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as State-of-the-Art (SotA) model-based techniques rely on precise knowledge of the vehicle's parameters, yet system identification in dynamic racing conditions is challenging due to varying track and tire conditions. Traditional methods require extensive operational ranges, often impractical in racing scenarios. Machine Learning (ML)-based methods, while improving performance, struggle with generalization and depend on accurate initialization. This paper introduces a novel on-track system identification algorithm, incorporating a Neural Network (NN) for error correction, which is then employed for traditional system identification with virtually generated data. Crucially, the process is iteratively reapplied, with tire parameters updated at each cycle, leading to notable improvements in accuracy in tests on a scaled vehicle. Experiments show that it is possible to learn a tire model without prior knowledge with only 30 seconds of driving data, and 3 seconds of training time. This method demonstrates greater one-step prediction accuracy than the baseline Nonlinear Least Squares (NLS) method under noisy conditions, achieving a 3.3x lower Root Mean Square Error (RMSE), and yields tire models with comparable accuracy to traditional steady-state system identification. Furthermore, unlike steady-state methods requiring large spaces and specific experimental setups, the proposed approach identifies tire parameters directly on a race track in dynamic racing environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1984-1991"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992982","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

Symbolic Manipulation Planning With Discovered Object and Relational Predicates 使用已发现对象和关系谓词的符号操作规划

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527338

Alper Ahmetoglu;Erhan Oztop;Emre Ugur

{"title":"Symbolic Manipulation Planning With Discovered Object and Relational Predicates","authors":"Alper Ahmetoglu;Erhan Oztop;Emre Ugur","doi":"10.1109/LRA.2025.3527338","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527338","url":null,"abstract":"Discovering the symbols and rules that can be used in long-horizon planning from a robot's unsupervised exploration of its environment and continuous sensorimotor experience is a challenging task. The previous studies proposed learning symbols from single or paired object interactions and planning with these symbols. In this work, we propose a system that learns rules with discovered object and relational symbols that encode an arbitrary number of objects and the relations between them, converts those rules to Planning Domain Description Language (PDDL), and generates plans that involve affordances of the arbitrary number of objects to achieve tasks. We validated our system with box-shaped objects in different sizes and showed that the system can develop a symbolic knowledge of pick-up, carry, and place operations, taking into account object compounds in different configurations, such as boxes would be carried together with a larger box that they are placed on. We also compared our method with other symbol learning methods and showed that planning with the operators defined over relational symbols gives better planning performance compared to the baselines.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1968-1975"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992980","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

Design and Analysis of a Hybrid Actuator With Resilient Origami-Inspired Hinges 弹性折纸铰链混合驱动器的设计与分析

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527282

Seunghoon Yoo;Hyunjun Park;Youngsu Cha

引用次数: 0

Incorporating Point Uncertainty in Radar SLAM 雷达SLAM中点不确定性的引入

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527344

Yang Xu;Qiucan Huang;Shaojie Shen;Huan Yin

引用次数: 0

Real-Time Sit-to-Stand Phase Classification With a Mobile Assistive Robot From Close Proximity Utilizing 3D Visual Skeleton Recognition 利用三维视觉骨骼识别的移动辅助机器人近距离实时坐姿-站立相位分类

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527280

Anas Mahdi;Zonghao Dong;Jonathan Feng-Shun Lin;Yue Hu;Yasuhisa Hirata;Katja Mombaur

{"title":"Real-Time Sit-to-Stand Phase Classification With a Mobile Assistive Robot From Close Proximity Utilizing 3D Visual Skeleton Recognition","authors":"Anas Mahdi;Zonghao Dong;Jonathan Feng-Shun Lin;Yue Hu;Yasuhisa Hirata;Katja Mombaur","doi":"10.1109/LRA.2025.3527280","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527280","url":null,"abstract":"Sit-to-stand (STS) transfer is a fundamental but challenging movement that plays a vital role in older adults' daily activities. The decline in muscular strength and coordination ability can result in difficulties performing STS and, therefore, the need for mobility assistance by humans or assistive devices. Robotics rollators are being developed to provide active mobility assistance to older adults, including STS assistance. In this paper, we consider the robotic walker SkyWalker, which can provide active STS assistance by moving the handles upwards and forward to bring the user to a standing configuration. In this context, it is crucial to monitor if the user is performing the STS and adapt the rollator's control accordingly. To achieve this, we utilized a standard vision-based method for estimating the human pose during the STS movement using Mediapipe pose tracking. Since estimating a user's state from extreme proximity to the camera is challenging, we compared the pose identification results from Mediapipe to ground truth data obtained from Vicon marker-based motion capture to assess accuracy and reliability of the STS motion. The fourteen kinematic features critical for accurate pose estimation were selected based on literature review and the specific requirements of our robot's STS method. By employing these features, we have implemented a phase classification system that enables the SkyWalker to classify the user's STS phase in real-time. The selected kinematics from vision-based human state estimation method and trained classifier can be furthermore generalized to other types of motion support, including adaptive STS path planning and emergency stops for safety insurance during STS.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2160-2167"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993211","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

MotIF: Motion Instruction Fine-Tuning

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527290

Minyoung Hwang;Joey Hejna;Dorsa Sadigh;Yonatan Bisk

{"title":"MotIF: Motion Instruction Fine-Tuning","authors":"Minyoung Hwang;Joey Hejna;Dorsa Sadigh;Yonatan Bisk","doi":"10.1109/LRA.2025.3527290","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527290","url":null,"abstract":"While success in many robotics tasks can be determined by only observing the final state and how it differs from the initial state – e.g., if an apple is picked up – many tasks require observing the full motion of the robot to correctly determine success. For example, brushing hair requires repeated strokes that correspond to the contours and type of hair. Prior works often use off-the-shelf vision-language models (VLMs) as success detectors; however, when success depends on the full trajectory, VLMs struggle to make correct judgments for two reasons. First, modern VLMs often use single frames, and thus cannot capture changes over a full trajectory. Second, even if we provide state-of-the-art VLMs with an input of multiple frames, they still fail to correctly detect success due to a lack of robot data. Our key idea is to fine-tune VLMs using abstract representations that are able to capture trajectory-level information such as the path the robot takes by overlaying keypoint trajectories on the final image. We propose motion instruction fine-tuning (MotIF), a method that fine-tunes VLMs using the aforementioned abstract representations to semantically ground the robot's behavior in the environment. To benchmark and fine-tune VLMs for robotic motion understanding, we introduce the MotIF-1K dataset containing 653 human and 369 robot demonstrations across 13 task categories with motion descriptions. MotIF assesses the success of robot motion given task and motion instructions. Our model significantly outperforms state-of-the-art API-based single-frame VLMs and video LMs by at least twice in F1 score with high precision and recall, generalizing across unseen motions, tasks, and environments. Finally, we demonstrate practical applications of MotIF in ranking trajectories on how they align with task and motion descriptions.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2287-2294"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106692","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

Online Resynthesis of High-Level Collaborative Tasks for Robots With Changing Capabilities 具有变化能力的机器人高水平协同任务的在线再合成

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527337

Amy Fang;Tenny Yin;Hadas Kress-Gazit

引用次数: 0

GelBelt: A Vision-Based Tactile Sensor for Continuous Sensing of Large Surfaces GelBelt：用于大表面连续传感的基于视觉的触觉传感器

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527306

Mohammad Amin Mirzaee;Hung-Jui Huang;Wenzhen Yuan

引用次数: 0

PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain

IF 4.6 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI: 10.1109/LRA.2025.3527285

Xiaoyi Cai;James Queeney;Tong Xu;Aniket Datar;Chenhui Pan;Max Miller;Ashton Flather;Philip R. Osteen;Nicholas Roy;Xuesu Xiao;Jonathan P. How

{"title":"PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain","authors":"Xiaoyi Cai;James Queeney;Tong Xu;Aniket Datar;Chenhui Pan;Max Miller;Ashton Flather;Philip R. Osteen;Nicholas Roy;Xuesu Xiao;Jonathan P. How","doi":"10.1109/LRA.2025.3527285","DOIUrl":"https://doi.org/10.1109/LRA.2025.3527285","url":null,"abstract":"Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training. Existing methods utilize techniques like evidential deep learning to quantify model uncertainty, helping to identify and avoid out-of-distribution terrain. However, always avoiding out-of-distribution terrain can be overly conservative, e.g., when novel terrain can be effectively analyzed using a physics-based model. To overcome this challenge, we introduce Physics-Informed Evidential Traversability (PIETRA), a self-supervised learning framework that integrates physics priors directly into the mathematical formulation of evidential neural networks and introduces physics knowledge implicitly through an uncertainty-aware, physics-informed training loss. Our evidential network seamlessly transitions between learned and physics-based predictions for out-of-distribution inputs. Additionally, the physics-informed loss regularizes the learned model, ensuring better alignment with the physics model. Extensive simulations and hardware experiments demonstrate that PIETRA improves both learning accuracy and navigation performance in environments with significant distribution shifts.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 3","pages":"2359-2366"},"PeriodicalIF":4.6,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106719","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