Papers
3
Total Citations
14
H-Index
2
About
Xiaopan Zhang is a leading researcher at the intersection of robotics, artificial intelligence, and agricultural automation. Their work focuses on solving fundamental challenges in multi-agent coordination and long-horizon task planning, with a particular emphasis on deploying legged robots in complex, real-world environments. Zhang’s most impactful contribution is the development of **LaMMA-P**, a novel framework that leverages Large Language Models (LLMs) to generate PDDL (Planning Domain Definition Language) plans for multi-robot teams. This work, already garnering 10 citations since its 2025 publication, addresses the critical bottleneck of translating natural language instructions into robust, long-horizon task allocations—a key step toward truly autonomous robot swarms. Zhang has also pioneered the use of quadruped robots in precision agriculture, notably demonstrating how legged robots can enhance human picker efficiency in delicate strawberry harvesting. Further advancing the field, their research on travel time and arrival time coordination provides a rigorous mathematical foundation for task allocation in legged-robot teams, ensuring mission resilience and efficiency. By bridging high-level AI planning with low-level robot control, Zhang is shaping the future of collaborative, autonomous systems.
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