Jiaping Xiao
Papers
2
Total Citations
21
H-Index
2
About
Jiaping Xiao is advancing the frontier of multi-robot coordination, with a focus on heterogeneous aerial-ground systems for critical applications like search and rescue. His work addresses a fundamental challenge: enabling effective collaboration between drones and ground robots without relying on high-bandwidth communication or heavy onboard sensors. In his highly cited 2025 paper on "Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning" (17 citations), Xiao introduces a learning-based framework that allows robots to autonomously explore unknown environments and locate targets, even under severe communication constraints. His 2024 work on "Air–Ground Collaborative Control for Angle-Specified Heterogeneous Formations" (4 citations) further tackles the practical problem of maintaining precise geometric formations between aerial and ground units—a capability essential for coordinated sensing and manipulation. By combining deep reinforcement learning with formation control theory, Xiao is enabling a new class of lightweight, scalable multi-robot teams that can operate in GPS-denied or disaster-stricken areas. His research sits at the intersection of robotics, control systems, and artificial intelligence, offering practical solutions for real-world deployment where traditional methods fall short.
Research Focus
Key Achievements
Top Papers
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