Yulun Zhang

Ph.D. Student

Carnegie Mellon University

Biography

My name is Yulun Zhang (张宇伦). I am a third year Ph.D. student in Robotics Institute at the Carnegie Mellon University, advised by Professor Jiaoyang Li. Currently I am focusing on environment optimization for Multi-Agent Path Finding (MAPF) algorithms using Quality-Diversity (QD) optimization, evolutionary computation, and generative modeling methods.

Previously, I was a master/undergrad student majoring in Computer Science at the University of Southern California. I was working in ICAROS lab at USC with Professor Stefanos Nikolaidis and Dr. Matthew Fontaine on surrogate assisted QD optimization and scenario generation for human-robot coordination. I was also working in RESL at USC with Dr. Ryan Julian and Dr. K.R. Zentner on transfer learning for robotics.

In addition, I was working in the USC Interaction Lab with Professor Matt Rueben on socially assistive robotics as well as Professor William Halfond’s group on record and replay tools for Android.

Here is my most recent CV (updated Jan 2025).

I am also enthusiastic about photography, especially scenery photography. Check out my photography portfolio and instagram for some of my works.

Contact me: yulunzhang [at] cmu [dot] edu

Interests

Quality Diversity Optimization
Evolutionary Computation
Multi-Robot Coordination
Human-Robot Interaction

Education

Ph.D. in Robotics, 2022 - Present
Carnegie Mellon University
MSc in Computer Science, 2020 - 2022
University of Southern California
BSc in Computer Science, 2017 - 2021
University of Southern California

Featured Publications

$^U$ denotes mentored Undergrads.

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Yulun Zhang, He Jiang, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li. Guidance Graph Optimization for Lifelong Multi-Agent Path Finding. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), August 03–09, Jeju, Korea, 2024.

Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li. Arbitrarily Scalable Environment Generators via Neural Cellular Automata. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), December 10–16, New Orleans, LA, USA, 2023.

Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li. Multi-Robot Coordination and Layout Design for Automated Warehousing. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), August 19–25, Macao, China, 2023.

Yulun Zhang, Matthew C. Fontaine, Amy K. Hoover, Stefanos Nikolaidis. Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), July 9–13, Boston, MA, USA, 2022.

Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang, Bryon Tjanaka, Stefanos Nikolaidis. On the Importance of Environments in Human-Robot Coordination. In Proceedings of Robotics: Science and Systems (RSS), July 12–16, 2021.

Other Publications

$^U$ denotes mentored Undergrads.

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Jingtian Yan, Zhifei Li, William Kang, Yulun Zhang, Stephen Smith, Jiaoyang Li. Advancing MAPF towards the Real World: A Scalable Multi-Agent Realistic Testbed (SMART). Preprint, 2025.

Hongzhi Zang$^U$, Yulun Zhang, He Jiang, Zhe Chen, Daniel Harabor, Peter J. Stuckey, Jiaoyang Li. Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Feb 27-Mar 04, Philadelphia, PA, USA, 2025.

Cheng Qian$^U$, Yulun Zhang, Varun Bhatt, Matthew C. Fontaine, Stefanos Nikolaidis, Jiaoyang Li. A Quality Diversity Method to Automatically Generate Multi-Agent Path Finding Benchmark Maps. Preprint, 2024.

Paul Friedrich, Yulun Zhang, Michael Curry, Ludwig Dierks, Stephen McAleer, Jiaoyang Li, Tuomas Sandholm, Sven Seuken. Scalable Mechanism Design for Multi-Agent Path Finding. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), August 03–09, Jeju, Korea, 2024.

He Jiang, Yulun Zhang, Rishi Veerapaneni, Jiaoyang Li. Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities. In Proceedings of the Symposium on Combinatorial Search (SoCS), June 06-08, Kananaskis, Alberta, Canada, 2024.

Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Yulun Zhang, Nivedit Reddy Balam, Nathaniel Dennler, Sujay S. Garlanka, Nikitas Dimitri Klapsis, Stefanos Nikolaidis. pyribs: A Bare-Bones Python Library for Quality Diversity Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), July 15–19, Lisbon, Portugal, 2023.

K.R. Zentner, Ujjwal Puri, Yulun Zhang, Ryan C. Julian, Gaurav S. Sukhatme. Efficient Multi-Task Learning via Iterated Single-Task Transfer. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 23-27, Kyoto, Japan, 2022.

K.R. Zentner, Ryan C. Julian, Ujjwal Puri, Yulun Zhang, Gaurav S. Sukhatme. A Simple Approach to Continual Learning by Transferring Skill Parameters. Preprint, 2021.

Matthew Rueben, Mohammad Syed, Emily London, Mark Camarena, Eunsook Shin, Yulun Zhang, Timothy S. Wang, Thomas R. Groechel, Rhianna Lee, Maja J. Matarić. Long-Term, in-the-Wild Study of Feedback about Speech Intelligibility for K-12 Students Attending Class via a Telepresence Robot. In Proceedings of the International Conference on Multimodal Interaction (ICMI), October 18–22, Montréal, QC, Canada, 2021.

K.R. Zentner, Ryan C. Julian, Ujjwal Puri, Yulun Zhang, Gaurav S. Sukhatme. Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning. In NeurIPS 2020 Workshop: Challenges of Real World Reinforcement Learning, December 12, 2020.

Matthew Rueben, Thomas Groechel, Yulun Zhang, Gisele Ragusa, Maja J. Matarić. Increasing Telepresence Robot Operator Awareness of Speaking Volume Appropriateness: Initial Model Development. In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI), March 24-26, Cambridge, United Kingdom, 2020.