Chengyang He

Papers

Total Citations

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About

Chengyang He is an emerging researcher specializing in multi-agent pathfinding (MAPF) and learning-based approaches to autonomous navigation and coordination. His work addresses one of robotics' most fundamental challenges: enabling teams of agents to navigate complex environments efficiently and without collision, with direct applications in large-scale logistics, transportation, and robotic deployment. He is best known for developing ALPHA (Attention-based Long-horizon Pathfinding in Highly-structured Areas), a novel framework that leverages attention mechanisms to tackle the demanding problem of long-horizon pathfinding in structured environments. With 12 citations since its 2024 publication, this work has quickly attracted attention from the MAPF research community. His more recent contribution, SIGMA (Sheaf-Informed Geometric Multi-Agent Pathfinding), introduces sheaf theory and geometric reasoning into decentralized learning-based MAPF approaches, demonstrating his commitment to pushing mathematical frontiers in multi-agent systems. He's work reflects a clear intellectual trajectory: bridging deep learning architectures, geometric reasoning, and real-world robotic coordination. Though early in his career, his rapid output and interdisciplinary approach position him as a promising voice in the growing field of scalable, intelligent multi-agent systems.