Papers
19
Total Citations
899
H-Index
9
About
Hanlin Niu is a robotics and autonomous systems researcher whose work sits at the intersection of deep reinforcement learning, multi-robot coordination, and intelligent navigation. He has made particularly influential contributions to autonomous exploration, developing Voronoi-based frameworks that enable teams of networked robots to collaboratively map unknown environments — a paper that has garnered over 414 citations and stands as a landmark contribution to multi-agent systems research. His hierarchical deep learning control architecture for fast trajectory planning in mobile robots has similarly resonated with the community, accumulating 276 citations since 2022. Niu's research extends across collision avoidance in swarm systems, sim-to-real transfer strategies, and model-free path planning for robotic manipulators, demonstrating a breadth that bridges theoretical rigor with practical robotics applications. His bio-inspired and human-guided learning approaches reflect a commitment to safe, deployable autonomous systems. More recently, he has explored teleoperation and bimanual manipulation, proposing digital twin-enabled kinesthetic teaching systems and consensus-based distributed training algorithms. Across his body of work, Niu has established himself as a productive voice in making autonomous robots smarter, safer, and more adaptable to real-world uncertainty — with a cumulative citation impact that underscores his growing influence in the field.
Research Focus
Key Achievements
Top Papers
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- 8Virtual Kinesthetic Teaching for Bimanual Telemanipulation12 citations · 2021
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