Guoxiang Zhao is an Associate Professor with Shanghai Unviersity, Shanghai, China. His research focuses on robotic motion planning and multi-robot systems.
Guoxiang Zhao obtained his PhD degree from the School of Electrical Engineering and Computer Science at Pennsylvania State University in May 2022. He received his bachelor degree in mechanical engineering and automation from Shanghai Jiao Tong University, Shanghai, China in 2014 and his master degree in mechanical engineering from Purdue University, West Lafayette, IN in 2015. He was affliated with the Multi-agent Networks Laboratory led by Prof. Minghui Zhu. Before his current position, he was a Software Engineer with TuSimple, Inc. at San Diego, CA from 2022 to 2023.
Education
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Ph.D. in Electrical Engineering, 2022
Pennsylvania State University, University Park, PA, USA -
Master of Science in Mechanical Engineering, 2015
Purdue University, West Lafayette, IN, USA -
Bachelor of Engineering in Mechanical Engineering and Automation, 2014
Shanghai Jiao Tong University, Shanghai, China
Publications
Journal Papers
- Zhao, G., & Zhu, M. (2022). Scalable distributed algorithms for multi-robot near-optimal motion planning. Automatica, vol. 140, Article 108637. June 2022.
- Zhao, G., & Zhu, M. (2021). Pareto optimal multi-robot motion planning. IEEE Transactions on Automatic Control, vol. 66, no. 9, pp. 3984-3999, Sept. 2021.
Conference Papers
- Zhang, H., Zhao, G. & Ren, X. (2025). TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement Learning. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China. To appear.
- Yang, Q., Zhao, G., Ren, X. & Wang, X. (2025). LLM-DiSC: LLM-enabled Distributed and Safe Coordination of Multi-Robot Systems using Control Barrier Functions. In 2025 China Automation Congress (CAC), Harbin, China. (nominated for Best Paper) To appear.
- Jiang, Y., Zhao, G. & Ren, X. (2025). Safe Near-Optimal Reinforcement Learning for Robotic Motion Planning Using High Order Control Barrier Function. In 2025 IEEE 19th International Conference on Control & Automation (ICCA), 168-173, Tallinn, Estonia. (nominated for Best Student Paper)
- Wang, H., & Zhao, G. (2024). Risk-Aware Safe Feedback Motion Planning in Gaussian Splatting World. In 2024 IEEE International Conference on Unmanned Systems (ICUS), 447-452, Nanjing, China. (nominated for Best Student Paper)
- Lu, Y., Guo, Y., Zhao, G. & Zhu, M. (2021). Distributed safe reinforcement learning for multi-robot motion planning. In 29th Mediterranean Conference on Control and Automation, 1209-1214, Bari, Italy.
- Zhao, G. & Zhu, M. (2019). Scalable distributed algo-rithms for multi-robotnear-optimal motion planning. In 58th IEEE Conference on Decision and Control, 226–231, Nice, France.
- Zhao, G. & Zhu, M. (2018). Pareto optimal multi-robot motion planning. In 2018 American Control Conference, 4020-4025, Milwaukee, WI.
Working Papers
- Zhao, G., Jha, D. K., Wang, Y., & Zhu, M. (2023). iPolicy: Incremental Policy Algorithms for Feedback Motion Planning.
- Zhao, G., Lu, Y., & Zhu, M. Distributed optimal motion planning with dependent goals.
Patents
- Hung, W.-T., Wang, A., Xu, K., Zhao, G., Zhang, B., Yu, N. (2023). Systems and methods of maintaining map for autonomous driving, U.S. Patent and Trademark Office. Submitted.