Papers
12
Total Citations
104
H-Index
5
About
Yulun Zhang is a robotics researcher whose work spans multi-agent coordination, human-robot interaction, and automated warehouse optimization. His most influential contributions lie in the domain of Multi-Agent Path Finding (MAPF), where he has pushed the boundaries of how large fleets of robots can be efficiently coordinated in real-world settings. His highly cited work on multi-robot coordination and warehouse layout design challenges the conventional assumption that better algorithms alone drive warehouse throughput, demonstrating instead that environment design itself plays a critical role — a perspective that has garnered significant attention with nearly 30 combined citations across related publications. Zhang also made a mark by winning the 2023 League of Robot Runners LMAPF competition, with insights from that achievement informing his research on scaling lifelong MAPF to realistic deployments. Beyond multi-robot systems, Zhang has explored human-robot teaming, showing how environmental factors shape coordination behaviors, and has contributed meaningfully to telepresence robotics, developing tools that help homebound K-12 students communicate more effectively in classroom settings. His work on neural cellular automata for scalable environment generation further reflects his creative approach to solving complex robotics challenges through interdisciplinary methods.
Research Focus
Key Achievements
Top Papers
- 1On the Importance of Environments in Human-Robot Coordination27 citations · 2021
- 2Multi-Robot Coordination and Layout Design for Automated Warehousing19 citations · 2023
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- 6On the Importance of Environments in Human-Robot Coordination5 citations · 2021
- 7Arbitrarily Scalable Environment Generators via Neural Cellular Automata5 citations · 2023
- 8Efficient Multi-Task Learning via Iterated Single-Task Transfer4 citations · 2022
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- 10Multi-Robot Coordination and Layout Design for Automated Warehousing3 citations · 2023