ICRA
Collection
Accepted papers for ICRA (IEEE International Conference on Robotics and Automation), one dataset per year. • 14 items • Updated
title string | program_url string | authors list | keywords list | abstract large_string | paper_code string | arxiv_id string | arxiv_id_source string | embedding list |
|---|---|---|---|---|---|---|---|---|
Real-Time Sit-To-Stand Phase Classification with a Mobile Assistive Robot from Close Proximity Utilizing 3D Visual Skeleton Recognition | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_01 | [
"Mahdi, Anas",
"Dong, Zonghao",
"Lin, Jonathan Feng-Shun",
"Hu, Yue",
"Hirata, Yasuhisa",
"Mombaur, Katja"
] | [
"Physically Assistive Devices",
"Physical Human-Robot Interaction"
] | 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 ... | TuI1I.1 | null | null | [
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Manydepth2: Motion-Aware Self-Supervised Monocular Depth Estimation in Dynamic Scenes | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_02 | [
"Zhou, Kaichen",
"Bian, Jiawang",
"Zheng, Jian-Qing",
"Zhong, Jia-Xing",
"Xie, Qian",
"Markham, Andrew",
"Trigoni, Niki"
] | [
"SLAM",
"Visual-Inertial SLAM",
"Visual Learning"
] | Despite advancements in self-supervised monocular depth estimation, challenges persist in dynamic scenarios due to the dependence on assumptions about a static world. In this paper, we present Manydepth2, to achieve precise depth estimation for both dynamic objects and static backgrounds, all while maintaining computat... | TuI1I.2 | 2312.15268 | title_snapshot | [
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FAST-LIEO: Fast and Real-Time LiDAR-Inertial-Event-Visual Odometry | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_03 | [
"Wang, Zirui",
"Ge, Yangtao",
"Dong, Kewei",
"Chen, I-Ming",
"Wu, Jing"
] | [
"SLAM",
"Localization",
"Sensor Fusion"
] | Unlike standard camera that relies on exposure to obtain output frame by frame, event camera only output an event when the change of brightness intensity in a pixel exceed a threshold, and the outputs of different pixels are independent to each other. Benefited from its bio-inspired design, event camera has the advanta... | TuI1I.3 | null | null | [
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... |
Diversity-Aware Crowd Model for Robust Robot Navigation in Human Populated Environment | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_04 | [
"Wu, Jiaxu",
"Wang, Yusheng",
"Chen, Tong",
"Jiang, Jun",
"Wang, Yongdong",
"An, Qi",
"Yamashita, Atsushi"
] | [
"Autonomous Vehicle Navigation",
"Human-Aware Motion Planning",
"Reinforcement Learning"
] | Robot navigation in human-populated environments poses challenges due to the diversity of human behaviors and the unpredictability of human paths. However, existing Reinforcement Learning (RL)-based methods often rely on simulators that lack sufficient diversity in human behavior, resulting in navigation policies that ... | TuI1I.4 | null | null | [
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On Motion Blur and Deblurring in Visual Place Recognition | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_05 | [
"Ismagilov, Timur",
"Ferrarini, Bruno",
"Milford, Michael J",
"Tuyen, Nguyen Tan Viet",
"Ramchurn, Sarvapali",
"Ehsan, Shoaib"
] | [
"Localization",
"Vision-Based Navigation",
"Data Sets for Robotic Vision"
] | Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, the impact of motion b... | TuI1I.5 | 2412.07751 | title_snapshot | [
0.01419668085873127,
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0.02762085571885109,
0.05652868002653122,
0.051142316311597824,
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-0.015036691911518574,
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Far-Field Image-Based Traversability Mapping for a Priori Unknown Natural Environments | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_06 | [
"Fahnestock, Ethan",
"Fuentes, Erick",
"Prentice, Samuel",
"Vasilopoulos, Vasileios",
"Osteen, Philip",
"Howard, Thomas",
"Roy, Nicholas"
] | [
"Vision-Based Navigation",
"Field Robots"
] | While navigating unknown environments, robots rely primarily on proximate features for guidance in decision making, such as depth information from lidar or stereo to build a costmap, or local semantic information from images. The limited range over which these features can be used may result in poor robot behavior when... | TuI1I.6 | null | null | [
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Iterative Shaping of Multi-Particle Aggregates Based on Action Trees and VLM | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_07 | [
"Lee, Hoi-Yin",
"Zhou, Peng",
"Duan, Anqing",
"Yang, Chenguang",
"Navarro-Alarcon, David"
] | [
"Bimanual Manipulation",
"Manipulation Planning"
] | In this paper, we address the problem of manipulating multi-particle aggregates using a bimanual robotic system. Our approach enables the autonomous transport of dispersed particles through a series of shapingand pushing actions using robotically-controlled tools. Achieving this advanced manipulation capability present... | TuI1I.7 | 2501.13507 | title_snapshot | [
0.004250749479979277,
-0.0060190181247889996,
0.004853656981140375,
-0.005837239325046539,
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0.... |
What Matters in Learning a Zero-Shot Sim-To-Real RL Policy for Quadrotor Control? a Comprehensive Study | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_08 | [
"Chen, Jiayu",
"Yu, Chao",
"Xie, Yuqing",
"Gao, Feng",
"Chen, Yinuo",
"Yu, Shu'ang",
"Tang, Wenhao",
"Ji, Shilong",
"Mu, Mo",
"Wu, Yi",
"Yang, Huazhong",
"Wang, Yu"
] | [
"Reinforcement Learning",
"Machine Learning for Robot Control",
"Aerial Systems: Applications"
] | Precise and agile flight maneuvers are essential for quadrotor applications, yet traditional control methods are limited by their reliance on flat trajectories or computationally intensive optimization. Reinforcement learning (RL)-based policies offer a promising alternative by directly mapping observations to actions,... | TuI1I.8 | 2412.11764 | title_snapshot | [
-0.00394812598824501,
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A Hyperspectral Imaging Guided Robotic Grasping System | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_09 | [
"Sun, Zheng",
"Dong, Zhipeng",
"Wang, Shixiong",
"Chu, Zhongyi",
"Chen, Fei"
] | [
"Perception for Grasping and Manipulation",
"Software-Hardware Integration for Robot Systems",
"Grasping"
] | Hyperspectral imaging is an advanced technique for precisely identifying and analyzing materials or objects. However, its integration with robotic grasping systems has so far been explored due to the deployment complexities and prohibitive costs. Within this paper, we introduce a novel hyperspectral imaging-guided robo... | TuI1I.9 | 2512.05578 | title_snapshot | [
-0.02241765893995762,
-0.014714508317410946,
-0.023332931101322174,
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-0.04302654042840004,
-0.027299338951706886,
-0.0505545400083065,
0.0... |
Zero-Shot Denoiser for Enhanced Acoustic Inspection: Blind Signal Separation and Text-Guided Audio Reconstruction | https://ras.papercept.net/conferences/conferences/ICRA26/program/ICRA26_ContentListWeb_3.html#tui1i_10 | [
"Shoda, Koki",
"Louhi Kasahara, Jun Younes",
"An, Qi",
"Yamashita, Atsushi"
] | [
"Robotics and Automation in Construction",
"Industrial Robots",
"Surveillance Robotic Systems"
] | Acoustic inspection is crucial for infrastructure maintenance, but its effectiveness is often hampered by environmental noise. Conventional denoising methods rely on prior knowledge or training data, limiting their practicability. This paper presents Zero-Shot Denoiser, a novel approach achieving noise reduction withou... | TuI1I.10 | null | null | [
0.010374878533184528,
0.022427581250667572,
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0.044407542794942856,
-0.03125040605664253,
0.0142... |
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