Qingbiao Li
Papers
14
Total Citations
969
H-Index
10
About
Qingbiao Li is a robotics and artificial intelligence researcher whose work sits at the intersection of multi-robot systems, graph neural networks, and autonomous navigation. His research has fundamentally advanced how robots communicate and coordinate in complex, dynamic environments, with a particular focus on decentralized multi-robot path planning — a critical challenge in modern logistics, transportation, and autonomous systems. Li's most celebrated contributions involve applying Graph Neural Networks (GNNs) to enable robot teams to share information intelligently and make collective decisions without centralized control. His 2020 paper on GNNs for decentralized multi-robot path planning has accumulated over 263 citations, while his globally guided reinforcement learning approach for dynamic environments has attracted 343 citations, demonstrating the field's appetite for scalable, learning-based solutions. His follow-up work on message-aware graph attention networks further refined large-scale coordination strategies, garnering 182 citations. Beyond navigation, Li has extended his expertise to tactile-based dexterous manipulation and real-world deployment of GNN-driven policies, bridging the gap between simulation and physical robot systems. His research in surgical robotics demonstrates a versatile methodological toolkit. With nearly 950 cumulative citations and a growing body of work, Li is establishing himself as a leading voice in intelligent, cooperative robotics systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Graph Neural Networks for Decentralized Multi-Robot Path Planning263 citations · 2020
- 3Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning182 citations · 2021
- 4
- 5Graph Neural Networks for Decentralized Multi-Robot Target Tracking27 citations · 2022
- 6
- 7Graph Neural Networks for Decentralized Multi-Robot Path Planning23 citations · 2019
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