Postdoctoral Researcher, Ph.D., The University of Hong Kong Robot Learning · Autonomous Navigation · Motion Planning · Embodied AI Email · Google Scholar · GitHub
I am a Postdoctoral Researcher in the School of Computing and Data Science at The University of Hong Kong (HKU), working with Prof. Hengshuang Zhao. I received my Ph.D. in Computer Science from HKU, supervised by Prof. Jia Pan and Prof. Qi Hao. 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 3.2K GitHub stars. Representative work includes NeuPAN (T-RO 2025), RDA Planner (RA-L 2023), RL-RVO-NAV (RA-L 2022), and the #1 ranked 2D robotics simulator: IR-SIM.
Research InterestsRobot Learning · Autonomous Navigation · Embodied AI · Motion Planning · Reinforcement Learning · Multi-Robot Systems · Foundation Models for Robotics
@article{han2026srlmpc,title={SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control},author={Han, Ruihua and Gao, Rui and Liu, Zhe and Wang, Xinyi and Chen, Chang and Wang, Shuai and Hao, Qi and Pan, Jia and Zhao, Hengshuang},journal={arXiv preprint arXiv:2608.21175},year={2026},eprint={2608.21175},archiveprefix={arXiv},primaryclass={cs.RO},code_coming_soon={true},}
preprint
IR-SIM: A Lightweight Skill-Native Simulator for Navigation, Learning, and Benchmarking
Ruihua Han, Shuai Wang , Chengyang Li , Rui Gao , Xinyi Wang , Zhe Liu , Guoliang Li , Yupu Lu , Qi Hao , Jia Pan , and Hengshuang Zhao
@article{han2026ir,title={IR-SIM: A Lightweight Skill-Native Simulator for Navigation, Learning, and Benchmarking},author={Han, Ruihua and Wang, Shuai and Li, Chengyang and Gao, Rui and Wang, Xinyi and Liu, Zhe and Li, Guoliang and Lu, Yupu and Hao, Qi and Pan, Jia and Zhao, Hengshuang},journal={arXiv preprint arXiv:2606.08729},year={2026},}
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
@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
@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
@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},}
@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},}
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
@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},}
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
@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},}
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.
Python binding for the ORCA (Optimal Reciprocal Collision Avoidance) library. Exposes a Pythonic API mirroring the original C++ interface and integrates with IR-SIM for visualization–enabling rapid prototyping of multi-agent navigation and collision avoidance algorithms.
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.
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).
ROS plugin implementing the ORCA velocity obstacle algorithm for multi-agent collision avoidance. Provides a Gazebo plugin for decentralized coordination in multi-robot systems.
Academic Service
Reviewer: IEEE Transactions on Robotics (T-RO); IEEE/ASME Transactions on Mechatronics (T-Mech); IEEE Robotics and Automation Letters (RA-L); Pattern Recognition; Autonomous Robots; IEEE International Conference on Robotics and Automation (ICRA); IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); American Control Conference (ACC); Conference on Neural Information Processing Systems (NeurIPS); Conference on Robot Learning (CoRL).
SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive ControlarXiv preprint arXiv:2608.21175, 2026
