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Zhiyun Lin

2 accepted papers

2026

Multi-Agent Reinforcement Learning With Spatial Structure Awareness for Topological Map-Based Path-Finding

RA-L 2026

Efficient Multi-Agent Path Finding (MAPF) is pivotal for warehouse logistics. While existing learning-based methods primarily rely on computationally intensive grid-based representations, topological maps offer a more flexible and scalable alternative - though this approach remains understudied. To

Cited by 0SourceScholar
2025

MambaGCN: Synergistic Integration of Graph Convolutional Networks and State Space Models for Point Cloud Processing

IROS 2025

Graph Neural Networks have emerged as a formidable tool for analyzing point clouds, leveraging their capacity to aggregate local features across multiple spatial scales via layered structures. However, a significant challenge lies in effectively and selectively integrating these multi-scale features

Cited by 1SourceScholar