Papers
18
Total Citations
339
H-Index
10
About
Keqin Li is a versatile and prolific researcher whose work spans robotics, artificial intelligence, neural computing, and autonomous systems. His scholarship centers on three interconnected domains: intelligent robot navigation and task planning, multimodal machine learning, and neural network-based mathematical solvers. Li has made significant contributions to task and motion planning (TAMP) for autonomous robots, with his 2023 survey already accumulating 83 citations, establishing him as a key synthesizer in this rapidly evolving field. His work on reinforcement learning-based warehouse navigation, combining Proximal Policy Optimization with classical graph algorithms, reflects a practical commitment to bridging theoretical AI with real-world logistics challenges. Equally notable is his research on zeroing neural networks for solving dynamic matrix equations, including Lyapunov and Sylvester equations, demonstrating deep mathematical rigor applicable to robotic control systems. Li has also advanced multimodal emotion recognition in conversational AI and explored digital twin frameworks for drone coordination in mobile networks. Across his portfolio, his consistent focus on efficiency, robustness, and real-world applicability makes his research particularly valuable to engineers and AI practitioners working at the intersection of intelligent systems and autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1Recent Trends in Task and Motion Planning for Robotics: A Survey83 citations · 2023
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