Profile Photo

Yiming Luo (罗一鸣)

I am a Ph.D. student at the University of Hong Kong, advised by Prof. Fu Zhang and working closely with Prof. Boyu Zhou. Currently, I am also a research intern at RobbyAnt, mentored by Prof. Yinghao Xu. I received my B.Eng. in Automation from Xi'an Jiaotong University.

My research targets real-world robotic applications. I'm now working on dexterous bimanual manipulation, from data collection devices to learning frameworks—to enable efficient robot learning from human demonstrations.

If you'd like to discuss research opportunity, collaboration, or anything related, feel free to reach out via email: yyluouomm@connect.hku.hk.

Email / Google Scholar / GitHub / Linkedin

Publications

Teaser Image
Causal World Modeling for Robot Control
Lin Li*, Qihang Zhang*, Yiming Luo*, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, Yujun Shen, Yinghao Xu†
Technical Report
Paper / Code / Project Page
We develop an autoregressive diffusion framework that architecturally unifies visual dynamics prediction and action inference within a single interleaved sequence.
Teaser Image
FERMI: Flexible Radio Mapping with a Hybrid Propagation Model and Scalable Autonomous Data Collection
Yiming Luo, Yunfei Wang, Hongming Chen, Chengkai Wu, Ximin Lyu, Jinni Zhou, Jun Ma, Fu Zhang, Boyu Zhou†
Robotics: Science and Systems, 2025
Paper / Code
A flexible radio mapping framework combines learned multi-path model with physics-based modeling as well as a scalable planning method for autonomous data collection.
Teaser Image
Star-searcher: A complete and efficient aerial system for autonomous target search in complex unknown environments
Yiming Luo, Zixuan Zhuang, Neng Pan, Chen Feng, Shaojie Shen, Fei Gao, Hui Cheng, Boyu Zhou†
IEEE Robotics and Automation Letters, 2024
Paper / Code / Video
An aerial system featuring specialized sensor suites, mapping, and planning modules to optimize target searching in unknown environments.
Teaser Image
H3-Mapping: Quasi-Heterogeneous Feature Grids for Real-Time Dense Mapping Using Hierarchical Hybrid Representation
Chenxing Jiang, Yiming Luo, Boyu Zhou†, Shaojie Shen
IEEE Robotics and Automation Letters, 2024
Paper / Code / Video
A NeRF-based dense mapping method that enables faster and higher-quality reconstruction using quasi-heterogeneous feature grids for varying levels of texture complexity.
Teaser Image
SOAR: Simultaneous Exploration and Photographing with Heterogeneous UAVs for Fast Autonomous Reconstruction
Mingjie Zhang*, Chen Feng*, Zengzhi Li, Guiyong Zheng, Yiming Luo, Zhu Wang, Jinni Zhou, Shaojie Shen, Boyu Zhou†
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
Paper / Code / Video
A LiDAR-Visual heterogeneous multi-UAV system specifically designed for fast autonomous reconstruction of complex environments.

Service

Conference Reviewer: RSS, ICRA, IROS

Journal Reviewer: RA-L

Misc

I’ve got a cute but silly Pomeranian. I love J-Pop and Cantonese pop songs—highly recommend them if you’re feeling the research stress(:

My Pomeranian dog