Han Ruihua (韩瑞华)

I am a Postdoctoral Researcher in the School of Computing and Data Science at The University of Hong Kong, supervised by Prof. Hengshuang Zhao. I received my Ph.D. in Computer Science from HKU, advised by Prof. Jia Pan and Prof. Qi Hao. Prior to that, I obtained my M.Eng. from Xiamen University and B.Eng. from Wuhan University of Technology.

My research lies at the intersection of motion planning, robot learning, embodied AI, and optimal control. I work to advance the ability of robot systems to navigate and operate safely in complex, real-world environments – integrating model-based optimization with learning-based methods to achieve both theoretically grounded and practically intelligent robotic behavior.

I am an active contributor to the open-source robotics community. My projects have been widely adopted in academia and industry, collectively receiving over 2.9K GitHub stars. Representative work includes NeuPAN (T-RO 2025), RDA Planner (RA-L 2023), RL-RVO-NAV (RA-L 2022), and IR-SIM – the #1 ranked 2D robotics simulator.

Research Interests: Robot Learning · Autonomous Navigation · Embodied AI · Motion Planning · Reinforcement Learning · Multi-Robot Systems · Foundation Models for Robotics

Selected Publications

T-RO

NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning

Ruihua Han , Shuai Wang , Shuaijun Wang , Zeqing Zhang , Jianjun Chen , Shijie Lin , Chengyang Li , Chengzhong Xu , Yonina C Eldar , Qi Hao , and Jia Pan

IEEE Transactions on Robotics (Popular Paper), 2025

Bib PDF video Website Code

@article{han2024neupan,
  title = {NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning},
  author = {Han, Ruihua and Wang, Shuai and Wang, Shuaijun and Zhang, Zeqing and Chen, Jianjun and Lin, Shijie and Li, Chengyang and Xu, Chengzhong and Eldar, Yonina C and Hao, Qi and Pan, Jia},
  journal = {IEEE Transactions on Robotics (Popular Paper)},
  year = {2025},
}

T-MECH

Edge Accelerated Robot Navigation With Collaborative Motion Planning

Guoliang Li* , Ruihua Han* , Shuai Wang , Fei Gao , Yonina C Eldar , and Chengzhong Xu

IEEE/ASME Transactions on Mechatronics, 2024

Bib PDF Code

@article{li2024edge,
  title = {Edge Accelerated Robot Navigation With Collaborative Motion Planning},
  author = {Li*, Guoliang and Han*, Ruihua and Wang, Shuai and Gao, Fei and Eldar, Yonina C and Xu, Chengzhong},
  journal = {IEEE/ASME Transactions on Mechatronics},
  year = {2024},
  publisher = {IEEE},
}

RA-L

Rda: An accelerated collision free motion planner for autonomous navigation in cluttered environments

Ruihua Han , Shuai Wang , Shuaijun Wang , Zeqing Zhang , Qianru Zhang , Yonina C Eldar , Qi Hao , and Jia Pan

IEEE Robotics and Automation Letters (RA-L), 2023

Bib PDF video Code

@article{han2023rda,
  title = {Rda: An accelerated collision free motion planner for autonomous navigation in cluttered environments},
  author = {Han, Ruihua and Wang, Shuai and Wang, Shuaijun and Zhang, Zeqing and Zhang, Qianru and Eldar, Yonina C and Hao, Qi and Pan, Jia},
  journal = {IEEE Robotics and Automation Letters (RA-L)},
  volume = {8},
  number = {3},
  pages = {1715--1722},
  year = {2023},
  publisher = {IEEE},
}

RA-L

Reinforcement learned distributed multi-robot navigation with reciprocal velocity obstacle shaped rewards

Ruihua Han , Shengduo Chen , Shuaijun Wang , Zeqing Zhang , Rui Gao , Qi Hao , and Jia Pan

IEEE Robotics and Automation Letters, 2022

Bib PDF video Code

@article{han2022reinforcement,
  title = {Reinforcement learned distributed multi-robot navigation with reciprocal velocity obstacle shaped rewards},
  author = {Han, Ruihua and Chen, Shengduo and Wang, Shuaijun and Zhang, Zeqing and Gao, Rui and Hao, Qi and Pan, Jia},
  journal = {IEEE Robotics and Automation Letters},
  volume = {7},
  number = {3},
  pages = {5896--5903},
  year = {2022},
  publisher = {IEEE},
}

ICRA

Cooperative multi-robot navigation in dynamic environment with deep reinforcement learning

Ruihua Han , Shengduo Chen , and Qi Hao

In 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020

Bib PDF video

@inproceedings{han2020cooperative,
  title = {Cooperative multi-robot navigation in dynamic environment with deep reinforcement learning},
  author = {Han, Ruihua and Chen, Shengduo and Hao, Qi},
  booktitle = {2020 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {448--454},
  year = {2020},
  organization = {IEEE},
}

IROS

A distributed range-only collision avoidance approach for low-cost large-scale multi-robot systems

Ruihua Han , Shengduo Chen , and Qi Hao

In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020

Bib PDF video

@inproceedings{han2020distributed,
  title = {A distributed range-only collision avoidance approach for low-cost large-scale multi-robot systems},
  author = {Han, Ruihua and Chen, Shengduo and Hao, Qi},
  booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {8020--8026},
  year = {2020},
  organization = {IEEE},
}

Open Source Projects:

IR-SIM 🥇 Rank #1 in 2D robotics simulators: A lightweight, extensible Python simulator supporting diverse robot kinematics (differential, Ackermann, omnidirectional), customizable sensors (LiDAR, odometry), and YAML-based configuration. Designed for rapid prototyping of motion planning and reinforcement learning algorithms. Adopted by HKU (COMP3356 Robotics) and SUSTech (Intelligent Robot Course) for teaching.
neupan_ros : ROS deployment of the NeuPAN planner—a neural-augmented MPC framework that unifies learning-based prediction with optimization-based control. Features Gazebo integration with ready-to-use navigation and imitation learning demos.
rda_ros : ROS wrapper for the RDA planner—an ADMM-based MPC enabling parallel collision avoidance computation for arbitrary convex shapes. Supports multiple kinematics and includes demos for Gazebo (dynamic obstacles) and CARLA (autonomous driving).
rvo_ros : ROS plugin implementing the ORCA velocity obstacle algorithm for multi-agent collision avoidance. Provides a Gazebo plugin for decentralized coordination in multi-robot systems.

Service

Reviewer: IEEE Transactions on Robotics (T-RO); IEEE Robotics and Automation Letters (RA-L); IEEE International Conference on Robotics and Automation (ICRA); IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); American Control Conference (ACC).