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期刊&会议

International Conference on Robotics and Automation · 会议 · Robotics

2026-03-05 至 2026-03-05 共收录 15
2603.04118 2026-03-05 cs.RO

Modeling and Control of a Pneumatic Soft Robotic Catheter Using Neural Koopman Operators

基于神经Koopman算子的气动软机器人导管建模与控制

Yiyao Yue, Noah Barnes, Lingyun Di, Olivia Young, Ryan D. Sochol, Jeremy D. Brown, Axel Krieger

机构 * Laboratory for Computational Sensing and Robotics(计算感知与机器人实验室) Johns Hopkins University(约翰霍普金斯大学) Department of Mechanical Engineering(机械工程系) University of Maryland(马里兰大学)

AI总结 本文提出基于神经Koopman算子的框架,用于提升软机器人导管的建模与控制精度,实现高精度定位与姿态控制。

Comments 8 pages, 6 figures. Accepted to IEEE International Conference on Robotics and Automation (ICRA) 2026

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2603.03957 2026-03-05 cs.RO

ArthroCut: Autonomous Policy Learning for Robotic Bone Resection in Knee Arthroplasty

ArthroCut:膝关节置换术中机器人骨切除的自主策略学习

Xu Lu, Yiling Zhang, Wenquan Cheng, Longfei Ma, Fang Chen, Hongen Liao

机构 * School of Biomedical Engineering, Tsinghua University(清华大学生物医学工程学院) School of Biomedical Engineering, Shanghai Jiao Tong University(上海交通大学生物医学工程学院) Longwood Valley MedTech

AI总结 ArthroCut通过结合术前和术中数据,实现膝关节置换术中骨切除的自主策略学习,提升机器人手术的自主性和可解释性。

Comments Accepted for publication at the 2026 IEEE International Conference on Robotics and Automation (ICRA)

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2603.03701 2026-03-05 cs.RO cs.AI cs.HC cs.SI

UrbanHuRo: A Two-Layer Human-Robot Collaboration Framework for the Joint Optimization of Heterogeneous Urban Services

UrbanHuRo: 一种用于异构城市服务联合优化的双层人机协作框架

Tonmoy Dey, Lin Jiang, Zheng Dong, Guang Wang

机构 * Department of Computer Science, Florida State University(佛罗里达州立大学计算机科学系) Department of Computer Science, Wayne State University(韦恩州立大学计算机科学系)

AI总结 UrbanHuRo提出一种双层人机协作框架,通过众包配送和城市感知优化异构城市服务,提升感知覆盖和快递员收入,减少逾期订单。

Comments 8 pages, 15 figures. This paper has been accepted by ICRA'26 as a regular paper

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2603.03627 2026-03-05 cs.RO

Touch2Insert: Zero-Shot Peg Insertion by Touching Intersections of Peg and Hole

Touch2Insert: 通过触摸连接器与孔的交点实现零样本针插入

Masaru Yajima, Yuma Shin, Rei Kawakami, Asako Kanezaki, Kei Ota

机构 * Institute of Science Tokyo(东京科学研究所) Mitsubishi Electric(三菱电机)

AI总结 Touch2Insert通过触觉传感实现无需特定训练的零样本针插入,适用于多种连接器形状,实验显示其在模拟和真实机器人中均表现出高精度和高成功率。

Comments Accepted by ICRA 2026 (IEEE International Conference on Robotics and Automation)

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2602.08251 2026-03-05 cs.RO

Aerial Manipulation with Contact-Aware Onboard Perception and Hybrid Control

具备接触感知的机载感知与混合控制的空中操作

Yuanzhu Zhan, Yufei Jiang, Muqing Cao, Junyi Geng

机构 * Department of Aerospace Engineering, Pennsylvania State University(宾夕法尼亚州立大学航空航天工程系) Robotics Institute, Carnegie Mellon University(卡内基梅隆大学机器人研究所)

AI总结 本文提出了一种无需外部运动捕捉的机载感知与混合控制方法,实现了接触丰富任务的精准运动跟踪和稳定接触力控制。

Comments 8 pages, 7 figures. Accepted by ICRA 2026

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2602.05596 2026-03-05 cs.RO

TOLEBI: Learning Fault-Tolerant Bipedal Locomotion via Online Status Estimation and Fallibility Rewards

TOLEBI:通过在线状态估计和易错奖励学习容错双足运动

Hokyun Lee, Woo-Jeong Baek, Junhyeok Cha, Jaeheung Park

机构 * Department of Intelligence and Information, Graduate School of Convergence Science and Technology, Seoul National University(智能信息系,融合科学与技术研究生院,首尔国立大学) Artificial Intelligence Institute (AIIS), Seoul National University(人工智能研究院(AIIS),首尔国立大学) Advanced Institutes of Convergence Technology (AICT), Suwon(融合技术高级研究院(AICT),Suwon)

AI总结 TOLEBI通过在线状态估计和易错奖励,首次提出基于学习的双足运动容错框架,提升机器人在现实环境中的运动稳定性与容错能力。

Comments Accepted for Publication at IEEE International Conference on Robotics and Automation (ICRA) 2026

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2512.07041 2026-03-05 cs.RO

CERNet: Class-Embedding Predictive-Coding RNN for Unified Robot Motion, Recognition, and Confidence Estimation

CERNet:用于统一机器人运动、识别和置信度估计的类嵌入预测编码RNN

Hiroki Sawada, Alexandre Pitti, Mathias Quoy

机构 * Equipe Traitement de l’Information et Systèmes Laboratory (ETIS Laboratory), CNRS UMR8051, CY Cergy-Paris Université, ENSEA, Cergy, France(信息与系统实验室(ETIS实验室),CNRS UMR8051,CY塞克-巴黎大学,ENSEA,塞克,法国)

AI总结 CERNet通过类嵌入预测编码RNN实现机器人运动生成、识别和置信度估计的统一模型,显著降低轨迹误差并提升实时识别准确率。

Comments Accepted for presentation at IEEE International Conference on Robotics and Automation (ICRA) 2026

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2510.19655 2026-03-05 cs.RO

LaViRA: Language-Vision-Robot Actions Translation for Zero-Shot Vision Language Navigation in Continuous Environments

LaViRA: 语言-视觉-机器人动作翻译用于连续环境中的零样本视觉语言导航

Hongyu Ding, Ziming Xu, Yudong Fang, You Wu, Zixuan Chen, Jieqi Shi, Jing Huo, Yifan Zhang, Yang Gao

机构 * School of Computer Science, Nanjing University(南京大学计算机科学学院) School of Intelligence Science and Technology, Nanjing University(南京大学智能科学与技术学院) Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所) University of Chinese Academy of Sciences(中国科学院大学)

AI总结 LaViRA通过分解动作层次结构,利用多模态大语言模型的优势,实现连续环境中零样本视觉语言导航的高效导航与泛化能力。

Comments ICRA 2026

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2509.18979 2026-03-05 cs.RO cs.CV

Category-Level Object Shape and Pose Estimation in Less Than a Millisecond

小于毫秒内的类别级物体形状和姿态估计

Lorenzo Shaikewitz, Tim Nguyen, Luca Carlone

机构 * Massachusetts Institute of Technology(麻省理工学院) Laboratory for Information and Decision Systems(信息与决策系统实验室) Boston University(波士顿大学)

AI总结 本文提出了一种快速求解器,通过自一致场迭代在毫秒级时间内实现类别级物体形状和姿态估计,结合线性主动形状模型和最大后验优化,提供高效的全局最优性证书。

Comments Accepted to ICRA 2026. This version contains appendices

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2509.16677 2026-03-05 cs.CV cs.LG cs.RO eess.IV

Segment-to-Act: Label-Noise-Robust Action-Prompted Video Segmentation Towards Embodied Intelligence

基于动作的视频分割:面向具身智能的标签噪声鲁棒动作引导分割

Wenxin Li, Kunyu Peng, Di Wen, Ruiping Liu, Mengfei Duan, Kai Luo, Kailun Yang

机构 * School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China(人工智能与机器人学院及机器人视觉感知与控制技术国家工程研究中心,湖南大学,中国) Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Germany(人机学与机器人研究所,卡尔斯鲁厄理工学院,德国)

AI总结 本研究首次探索基于动作的视频对象分割在标签噪声下的鲁棒性,引入两种噪声类型并提出并行掩码头机制以提升分割性能。

Comments Accepted to ICRA 2026. The established benchmark and source code will be made publicly available at https://github.com/mylwx/ActiSeg-NL

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2509.14516 2026-03-05 cs.RO

Event-LAB: Towards Standardized Evaluation of Neuromorphic Localization Methods

Event-LAB: 向神经形态定位方法的标准化评估迈进

Adam D. Hines, Alejandro Fontan, Michael Milford, Tobias Fischer

机构 * QUT Centre for Robotics, School of Electrical Engineering and Robotics, Queensland University of Technology(昆士兰理工大学机器人中心,电气与机器人工程学院,昆士兰理工大学) School of Natural Sciences, Macquarie University(自然科学院,麦觉理大学)

AI总结 Event-LAB提供了一个统一框架,用于在多个数据集上运行多种基于事件的定位方法,通过一致的参数设置实现公平比较。

Comments 8 pages, 6 figures, accepted to the IEEE International Conference on Robotics and Automation (ICRA) 2026

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2508.17986 2026-03-05 cs.RO

No Need to Look! Locating and Grasping Objects by a Robot Arm Covered with Sensitive Skin

无需观察!由敏感皮肤覆盖的机械臂进行物体定位与抓取

Karel Bartunek, Lukas Rustler, Matej Hoffmann

机构 * Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague(电子工程系控制学系,布拉格捷克技术大学)

AI总结 本研究提出了一种无需视觉输入,仅通过机械臂敏感皮肤接触实现物体定位与抓取的方法,实验显示其在真实机器人上的成功率为85.7%,且比传统方法快六倍。

Comments Karel Bartunek, Lukas Rustler: Authors contributed equally Accepted to IEEE ICRA 2026

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2508.03099 2026-03-05 cs.RO

Point2Act: Efficient 3D Distillation of Multimodal LLMs for Zero-Shot Context-Aware Grasping

Point2Act: 多模态大语言模型的高效3D蒸馏用于零样本情境感知抓取

Sang Min Kim, Hyeongjun Heo, Junho Kim, Yonghyeon Lee, Young Min Kim

机构 * Seoul National University(首尔国立大学) Massachusetts Institute of Technology(麻省理工学院)

AI总结 Point2Act通过多模态大语言模型高效蒸馏实现零样本情境感知抓取,生成空间定位响应以支持实际操作任务。

Comments Accepted to ICRA 2026

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2603.03499 2026-03-05 cs.RO

Overlapping Domain Decomposition for Distributed Pose Graph Optimization

重叠域分解用于分布式姿态图优化

Aneesa Sonawalla, Yulun Tian, Jonathan P. How

机构 * MIT Department of Aeronautics and Astronautics(麻省理工学院航空与宇航科学系) The Charles Stark Draper Laboratory(查尔斯·斯塔克·德拉珀实验室) University of Michigan Robotics Department(密歇根大学机器人学系)

AI总结 ROBO通过重叠域分解方法,实现多机器人姿态图优化的高效分布式求解,显著提升收敛速度并适应不同通信环境。

Comments Accepted to IEEE International Conference on Robotics and Automation (ICRA) 2026

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2504.04289 2026-03-05 cs.RO cs.SY eess.SY

A Self-Supervised Learning Approach with Differentiable Optimization for UAV Trajectory Planning

一种结合可微优化的自监督学习方法用于无人机轨迹规划

Yufei Jiang, Yuanzhu Zhan, Harsh Vardhan Gupta, Chinmay Borde, Junyi Geng

机构 * Department of Aerospace Engineering, Pennsylvania State University(宾夕法尼亚州立大学航空航天工程系) Department of Engineering and Applied Science, University at Buffalo(布法罗大学工程与应用科学系)

AI总结 本文提出了一种结合可微优化的自监督学习方法,用于在三维环境中提升无人机轨迹规划的效率和鲁棒性。

Comments Accepted by ICRA 2026

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