Papers
4
Total Citations
18
H-Index
3
About
Wenhao Wang is a rising researcher at the intersection of artificial intelligence and robotics, whose work centers on integrating large language models (LLMs) with autonomous systems and advancing robot control and calibration. His most impactful contribution is the development of GSCE, a prompt framework that enhances reasoning reliability for LLM-driven drone control, which has already garnered 8 citations since its 2025 publication. This work addresses critical safety and reliability concerns in deploying LLMs for complex robotic tasks. Wang further advances LLM-agent capabilities with TRAD, a step-wise thought retrieval and aligned decision framework for web navigation and shopping agents (5 citations). In robotics, he has developed adaptive optimization methods for quadruped robot control using BP neural networks (4 citations) and proposed a certifiably correct algorithm for generalized robot-world and hand-eye calibration (2026). His research uniquely bridges the gap between high-level AI reasoning and low-level robotic control, offering practical solutions for autonomous systems. Wang’s work is particularly notable for its emphasis on reliability and correctness—from certifiable calibration algorithms to trustworthy LLM-driven control—making him a promising voice in the future of autonomous robotics.
Research Focus
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Top Papers
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