机器人 / 具身智能

Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning

Jannik Zürn, Wolfram Burgard, Abhinav Valada

专题命中 具身导航 ：navigation(abstract)；分类 cs.RO、cs.CV、cs.LG；robotics(journal_ref)

Comments 14 pages, 12 figures

Journal ref IEEE Transactions on Robotics (T-RO), vol. 37, no. 2, pp. 466-481, 2021

SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving

Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel

专题命中 具身导航 ：navigation(abstract)；分类 cs.RO、cs.CV、cs.LG；robotics(journal_ref)

Comments Code available at: https://github.com/wgcban/SPIN_RoadMapper.git

Journal ref IEEE Conference of Robotics and Automation (ICRA) 2022

Visual-Tactile Cross-Modal Data Generation using Residue-Fusion GAN with Feature-Matching and Perceptual Losses

Shaoyu Cai, Kening Zhu, Yuki Ban, Takuji Narumi

专题命中 具身导航 ：robotic(abstract)；分类 cs.RO、cs.AI、cs.CV；robotics(comments)

Comments 8 pages, 6 figures, Accepted by IEEE Robotics and Automation Letters

Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis

Daniel Seichter, Mona Köhler, Benjamin Lewandowski, Tim Wengefeld, Horst-Michael Gross

专题命中 具身导航 ：navigation(abstract)；分类 cs.RO、cs.CV、cs.LG；robotics(comments)

Comments To be published in IEEE International Conference on Robotics and Automation (ICRA) 2021; fixed reference in Fig. 4

Fast Uniform Dispersion of a Crash-prone Swarm

Michael Amir, Alfred M. Bruckstein

专题命中 具身导航 ：navigation(abstract)；robotic(abstract)；robotics(comments)

Comments to appear in the proceedings of RSS (Robotics: Science and Systems) 2020

Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation with Factor Graphs

整体融合：基于因子图的与任务和设置无关的机器人定位与状态估计

Julian Nubert, Turcan Tuna, Jonas Frey, Cesar Cadena, Katherine J. Kuchenbecker, Shehryar Khattak, Marco Hutter

机构 * ETH Zürich（苏黎世联邦理工学院） ； Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所） ； NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室） 纠错

专题命中 具身导航 ：robotic(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

AI总结 研究针对移动机器人定位与状态估计问题，提出整体融合方法，通过因子图公式融合多类型测量，注重局部平滑性与一致性，能在典型硬件上实现低延迟、平滑在线估计及低漂移全局定位，在多场景验证了框架有效性。

Comments 21 pages, 25 figures, 9 tables, Transactions on Robotics, accepted

Journal ref Transactions on Robotics 2026

Extreme dynamic symmetry enables omnidirectional and multifunctional robots

极端动态对称性实现全向多功能机器人

Jiaxun Liu, Boxi Xia, Boyuan Chen

机构 * Department of Mechanical Engineering and Materials Science, Duke University（杜克大学机械工程与材料科学系） ； Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系） ； Department of Computer Science, Duke University（杜克大学计算机科学系） 纠错

专题命中 具身导航 ：robotics(abstract,comments)；分类 cs.RO、cs.AI

AI总结 本文提出动态对称性概念，通过动态各向同性度量，在超过1000种模拟形态中发现高动态对称性可提升轨迹跟踪、任务成功率、鲁棒性等性能，并开发了Argus球形机器人系列验证近极端动态各向同性带来的全向运动、自适应地形、快速自稳定和抗故障能力。

Comments Published in Science Robotics (2026). Our project website is at:https://generalroboticslab.com/Argus

Journal ref Science Robotics 11, eaec1725 (2026)

Learning Geometric and Photometric Features from Panoramic LiDAR Scans for Outdoor Place Categorization

从全景LiDAR扫描中学习几何和光度特征用于户外场所分类

Kazuto Nakashima, Hojung Jung, Yuki Oto, Yumi Iwashita, Ryo Kurazume, Oscar Martinez Mozos

机构 * Graduate School of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电子工程研究生院） ； Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室） ； Faculty of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电子工程学部） ； Technical University of Cartagena（卡塔赫纳技术大学） 纠错

专题命中 具身导航 ：navigation(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

AI总结 本文提出利用CNN处理LiDAR获取的全景深度/反照率图像，通过构建MPO数据集实现户外场所分类，优于传统方法，验证了深度与反照率模态的联合使用有效性。

Comments Published in Advanced Robotics on 31 Jul 2018

Journal ref Advanced Robotics, 32(14):750-765, 2018

Learning Quadrupedal Locomotion for a Heavy Hydraulic Robot Using an Actuator Model

为重载液压机器人学习四足运动的致动器模型

Minho Lee, Hyeonseok Kim, Jin Tak Kim, Sangshin Park, Jeong Hyun Lee, Jungsan Cho, Jemin Hwangbo

机构 * Robotics and Artificial Intelligence Lab, KAIST（机器人与人工智能实验室，韩国科学技术院） ； Robotics Team, HYUNDAI Rotem（HYUNDAI Rotem机器人团队） ； Hydraulic Robot Laboratory, Human-Centric Robotics R&D Department, Korea Institue of Industrial Technology（液压机器人实验室，人本机器人研发部门，韩国工业技术研究院） 纠错

专题命中 具身导航 ：robotics(abstract,comments)；分类 cs.RO、cs.AI

AI总结 本研究提出了一种基于液压动力学的分析致动器模型，用于重载液压四足机器人在RL中的运动控制，实现了稳定的sim-to-real转移。

Comments 9 pages, Accepted to IEEE Robotics and Automation Letters (RA-L) 2025

Journal ref IEEE Robotics and Automation Letters (Volume: 10, Issue: 12, December 2025)

Trajectory prediction for heterogeneous agents: A performance analysis on small and imbalanced datasets

Tiago Rodrigues de Almeida, Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner, Johannes A. Stork, Martin Magnusson, Achim J. Lilienthal

专题命中 具身导航 ：robotics(abstract,comments)；分类 cs.RO、cs.LG

Comments This paper has been accepted to the IEEE Robotics and Automation Letters journal and presented at the 40th Anniversary of the IEEE International Conference on Robotics and Automation, which was held in Rotterdam, Netherlands on 23-26 September, 2024

Journal ref IEEE Robotics and Automation Letters ( Volume: 9, Issue: 7, July 2024)

Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm

Ziyu Zhang, Johann Laconte, Daniil Lisus, Timothy D. Barfoot

机构 * University of Toronto Institute for Aerospace Studies (UTIAS)（多伦多大学航空航天研究学院） 纠错

专题命中 具身导航 ：navigation(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.LG

Comments 9 pages (6 content, 1 reference, 2 appendix). 7 figures, accepted to 2025 IEEE International Conference on Robotics and Automation (ICRA)

Journal ref 2025 IEEE International Conference on Robotics and Automation (ICRA), Atlanta, GA, USA, 2025, pp. 15174-15180

iSLAM: Imperative SLAM

Taimeng Fu, Shaoshu Su, Yiren Lu, Chen Wang

机构 * Spatial AI & Robotics (SAIR) Lab, Institute for Artificial Intelligence and Data Science, Department of Computer Science and Engineering, University at Buffalo（空间人工智能与机器人实验室，人工智能与数据科学研究所，计算机科学与工程系，布法罗大学） 纠错

专题命中 具身导航 ：navigation(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

Comments The paper has been accepted by IEEE Robotics and Automation Letters (RA-L)

Journal ref IEEE Robotics and Automation Letters (RA-L), 2024

DynaVINS++: Robust Visual-Inertial State Estimator in Dynamic Environments by Adaptive Truncated Least Squares and Stable State Recovery

Seungwon Song, Hyungtae Lim, Alex Junho Lee, Hyun Myung

专题命中 具身导航 ：navigation(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

Comments 8 pages, 7 figures. S. Song, H. Lim, A. J. Lee and H. Myung, "DynaVINS++: Robust Visual-Inertial State Estimator in Dynamic Environments by Adaptive Truncated Least Squares and Stable State Recovery," in IEEE Robotics and Automation Letters, vol. 9, no. 10, pp. 9127-9134, Oct. 2024

Journal ref IEEE Robotics and Automation Letters, vol. 9, no. 10, pp. 9127-9134, Oct. 2024

UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT

Matej Petrlik, Pavel Petracek, Vit Kratky, Tomas Musil, Yurii Stasinchuk, Matous Vrba, Tomas Baca, Daniel Hert, Martin Pecka, Tomas Svoboda, Martin Saska

专题命中 具身导航 ：navigation(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.AI

Comments Submitted to Field Robotics Special Issue: DARPA Subterranean Challenge, Advancement and Lessons Learned from the Finals

Journal ref Field Robotics, vol. 3, no. 1 pp. 1-68, January, 2023

Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications

Mingyu Cai, Erfan Aasi, Calin Belta, Cristian-Ioan Vasile

专题命中 具身导航 ：navigation(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.LG

Comments IEEE Robotics and Automation Letters

Journal ref IEEE Robotics and Automation Letters, 2023

A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations

Sohan Rudra, Saksham Goel, Anirban Santara, Claudio Gentile, Laurent Perron, Fei Xia, Vikas Sindhwani, Carolina Parada, Gaurav Aggarwal

专题命中 具身导航 ：navigation(abstract)；分类 cs.RO、cs.LG；robotics(comments)；robot learning(comments)

Comments Shorter version accepted at NeurIPS 2022 Workshop on Robot Learning: Trustworthy Robotics

Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching

Justin Tomasi, Brandon Wagstaff, Steven L. Waslander, Jonathan Kelly

专题命中 具身导航 ：navigation(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

Comments In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Robotics and Automation (ICRA'21), Xi'an, China, May 30-Jun. 5, 2021

Journal ref IEEE Robotics and Automation Letters (RA-L), Vol. 6, No. 2, pp. 2028-2035, Apr. 2021

How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change

Lee Clement, Jonathan Kelly

专题命中 具身导航 ：robotics(abstract,comments)；分类 cs.RO、cs.CV

Comments In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Robotics and Automation (ICRA'18), Brisbane, Australia, May 21-25, 2018

Journal ref IEEE Robotics and Automation Letters (RA-L), Vol. 3, No. 3, pp. 2447-2454, Jul. 2018

Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing

Noha Radwan, Wolfram Burgard, Abhinav Valada

专题命中 具身导航 ：navigation(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

Comments The International Journal of Robotics Research (2020)

Journal ref The International Journal of Robotics Research (IJRR), vol. 39, no. 13, pp. 1567-1598, 2020

Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery

Margarita Grinvald, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Cesar Cadena, Roland Siegwart, Juan Nieto

专题命中 具身导航 ：robotic(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

Comments 8 pages, 4 figures. To be published in IEEE Robotics and Automation Letters (RA-L) and 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Accompanying video material can be found at http://youtu.be/Jvl42VJmYxg

Journal ref IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 3037-3044, July 2019

Robust Dense Mapping for Large-Scale Dynamic Environments

Ioan Andrei Bârsan, Peidong Liu, Marc Pollefeys, Andreas Geiger

专题命中 具身导航 ：robotic(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

Comments Presented at IEEE International Conference on Robotics and Automation (ICRA), 2018

Journal ref Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 7510--7517, 21-25 May 2018, Brisbane, QLD, Australia

AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

Ciro Potena, Raghav Khanna, Juan Nieto, Roland Siegwart, Daniele Nardi, Alberto Pretto

专题命中 具身导航 ：robotic(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

Comments Published in IEEE Robotics and Automation Letters, 2019

Journal ref IEEE Robotics and Automation Letters, Vol: 4, Issue: 2, April 2019, pages 1085-1092

Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators

Chen Wang, Tete Ji, Thien-Minh Nguyen, Lihua Xie

专题命中 具身导航 ：navigation(abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.CV

Comments 2018 International Conference on Robotics and Automation (ICRA 2018)

Journal ref 2018 IEEE International Conference on Robotics and Automation (ICRA)

Minimalist Visual Inertial Odometry

极简视觉惯性里程计

Francesco Pasti, Jeremy Klotz, Nicola Bellotto, Shree K. Nayar

机构 * Department of Information Engineering, University of Padua（帕多瓦大学信息工程系） ； Computer Science Department, Columbia University（哥伦比亚大学计算机科学系） 纠错

专题命中 具身导航 ：navigation(abstract)；分类 cs.RO、cs.CV、cs.LG

AI总结 本文提出了一种极简的平面里程计方法，通过四个视觉测量和一个IMU实现差分驱动机器人的鲁棒运动估计，展示了极简传感在高效准确平面里程计中的应用。

Comments This work has been submitted to the IEEE for possible publication

Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation

从CT阅片时的三维注视模式预测放射科医生的专业水平

Leila Khaertdinova, Anna Anikina, Claudia Mello-Thoms, Bulat Ibragimov

机构 * University of Copenhagen（哥本哈根大学） ； University of Iowa（爱荷华大学） 纠错

专题命中 具身导航 ：navigation(abstract)；分类 cs.AI、cs.CV、cs.LG

AI总结 该研究提出基于注视信息的Transformer框架，利用放射科医生CT阅片的三维注视模式，在182次阅片会话数据上实现0.91的ROC-AUC和0.86的F1分数，可客观评估放射科医生专业水平。

Comments Accepted at MICCAI 2026

Edge-Native Embodied Intelligence for Action-Aware Wireless Edge Networks

面向动作感知无线边缘网络的边缘原生具身智能

Yiru Wang, Chuanao Jiang, Jiahui Cui, Zide Fan, Lei Wang, Zehui Xiong, Dong In Kim

专题命中 具身导航 ：embodied agent(abstract)；navigation(abstract)

AI总结 该研究提出边缘原生具身智能（ENEI）框架，整合6G技术与具身智能，通过双向循环解决具身智能部署的资源与延迟问题，经案例验证可支撑自适应具身无线系统。

Efficient Dynamic Shielding for Parametric Safety Specifications

面向参数化安全规范的高效动态防护机制

Davide Corsi, Kaushik Mallik, Andoni Rodriguez, Cesar Sanchez

机构 * University of California, Irvine（加州大学尔湾分校） ； IMDEA Software Institute（IMDEA软件研究所） 纠错

专题命中 具身导航 ：navigation(abstract)；分类 cs.RO、cs.AI、cs.LG

AI总结 本文针对参数化安全规范提出动态防护机制，其离线设计耗时数分钟，在线适配速度较暴力重新计算方法快最多5倍，可用于未知区域机器人导航以应对安全规范的动态变化。

Journal ref International Symposium on Automated Technology for Verification and Analysis (ATVA) 2025, pp. 157-179. Cham: Springer Nature Switzerland

Moving Horizon Estimation for Underwater Target Tracking Based on Time-Difference-of-Arrival Measurements

基于到达时间差测量的水下目标跟踪的移动时域估计

Anton Tolstonogov, David Cabecinhas, Pedro Batista, Antonio Pascoal

专题命中 具身导航 ：navigation(abstract)；robotic(abstract)

AI总结 本文针对水下目标跟踪难题，研究基于到达时间差的移动时域估计方法，仿真显示其鲁棒性优于经典扩展卡尔曼滤波，可作为多智能体跟踪系统的实用组件。

Comments 6 pages, 2 figures. This work has been accepted to IFAC WC 2026 for publication

Dual-Force: Enhanced Offline Diversity Maximization under Imitation Constraints

Dual-Force：模仿约束下的增强型离线多样性最大化算法

Pavel Kolev, Marin Vlastelica, Georg Martius

专题命中 具身导航 ：navigation(abstract)；分类 cs.RO、cs.AI、cs.LG

AI总结 Dual-Force是一种离线算法，通过后继特征的VdW力目标和功能奖励编码，实现模仿约束下的多样化行为策略学习，在Solo12仿真任务中提升了性能与鲁棒性。

Smart strategies to navigate turbulent odor plumes reorienting to local wind

智能策略用于在湍流气味团中导航：重新定向到局部风

Lorenzo Piro, Maurizio Carbone, Luca Biferale, Massimo Cencini, Robin A. Heinonen, Marco Rando, Agnese Seminara

专题命中 具身导航 ：navigation(abstract)；robotic(abstract)

AI总结 本文提出了一种基于风的强化学习框架，用于在湍流中导航，通过局部风方向估计和时间间隔来选择动作，展示了在不同风条件下策略的有效性。