Papers
7
Total Citations
83
H-Index
4
About
Wanli Ni is a rising researcher whose work sits at the dynamic intersection of wireless communications, robotics, and artificial intelligence, with a particular focus on the Internet of Robotic Things (IoRT) and next-generation 6G systems. His research tackles fundamental challenges in autonomous robot communication, including signal degradation from physical blockages, dynamic mobility environments, and efficient resource allocation in complex multi-robot deployments. Ni's most significant contributions center on applying deep reinforcement learning — particularly federated and multi-agent variants — to jointly optimize robot trajectories and communication strategies without requiring prior environmental knowledge. His 2022 paper on federated deep reinforcement learning for RIS-assisted indoor robot communications has garnered 34 citations, establishing him as a notable voice in reconfigurable intelligent surface (RIS) research. He has consistently advanced the integration of RIS technology into robotic systems, culminating in comprehensive frameworks that address over-the-air federated learning and multi-functional RIS architectures for edge intelligence. Spanning industrial IoT, smart factories, and autonomous mobile robots, Ni's body of work — accumulating nearly 85 citations across his key publications — demonstrates meaningful impact in an emerging field where intelligent communications and autonomous robotics increasingly converge.
Research Focus
Key Achievements
Top Papers
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- 5Reconfigurable Intelligent Surface for Internet of Robotic Things4 citations · 2025
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