I am a third-year Ph.D. candidate in the Department of Mechanical and Automation Engineering at The Chinese University of Hong Kong (CUHK). As a member of the Legged Robot Lab (CUHKLRL), I work under the supervision of Professor Yunhui Liu. My research focuses on developing advanced control strategies for legged robots through reinforcement learning approaches. I am particularly interested in bridging the gap between theoretical robotics and practical applications, working to create more robust and adaptable control systems for complex robotic platforms.
Before joining CUHK, I received my B.E. degree in 2022 at Zhejiang University in Automation and Control.
My current research interests focus on robot learning, including:
Hongbo Zhang, Zhongyu Li, Xuanqi Zeng, Laura Smith, Kyle Stachowicz, Dhruv Shah, Linzhu Yue, Zhitao Song, Weipeng Xia, Sergey Levine, Koushil Sreenath, Yun-hui Liu
Under Review (2025)
This work develops a hierarchical RGBD-based traversability-aware navigation-locomotion framework using reinforcement learning.
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Hongbo Zhang*, Xiangyu Chu*, Yanlin Chen, Yunxi Tang, Linzhu Yue, Yun-Hui Liu, and Kwok Wai Samuel Au
Under Review (2025)
This work presents the application of reinforcement learning to improve the performance of a highly dynamic hopping system with a parallel mechanism.
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Linzhu Yue, Zhitao Song, Jinhu Dong, Zhongyu Li, Hongbo Zhang, Lingwei Zhang, Xuanqi Zeng, Koushil Sreenath, Yun-hui Liu
Under Review (2025)
This paper proposes a general and complete cascade online optimization framework for omnidirectional jumping for quadruped robots.
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Gilbert Feng*, Hongbo Zhang*, Zhongyu Li, Xue Bin Peng, Bhuvan Basireddy, Linzhu Yue, Zhitao Song, Lizhi Yang, Yunhui Liu, Koushil Sreenath, Sergey Levine
Conference on Robot Learning (CoRL), 2022
We show that by training a controller on this large set of simulated robots, our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots with diverse morphologies.
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Xuanqi Zeng, Hongbo Zhang, Linzhu Yue, Zhitao Song, Lingwei Zhang, Yun-Hui Liu
International Conference on Robotics and Automation (ICRA), 2024
We propose the controller of integrating a data-driven error model into traditional MPC for quadruped robots.
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Linzhu Yue, Lingwei Zhang, Zhitao Song, Hongbo Zhang, Jinhu Dong, Xuanqi Zeng, Yun-Hui Liu
International Conference on Intelligent Robots and Systems (IROS), 2024
We propose an omnidirectional jumping framework that generates and tracks aerial motions for quadrupedal robots
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Linzhu Yue, Zhitao Song, Hongbo Zhang, Xuanqi Zeng, Lingwei Zhang, Yun-Hui Liu
International Conference on Intelligent Robots and Systems (IROS), 2023
We propose a time-friendly online motion planning framework for quadruped jumping based on the meta-heuristic Differential evolution algorithm.
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Hongbo Zhang*, Yunshuang Li*, Yipin Guo*, Xinyi Chen, Qinyuan Ren
International Conference on Social Robotics (ICSR), 2021
A neural network model of Cerebellum based on spiking neuron networks (SNNs) is designed to control a 1-DOF robot arm driven by Pneumatic artificial muscles (PAMs).
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