Yuanjiang Hu
Papers
2
Total Citations
9
H-Index
2
About
Yuanjiang Hu is a rising researcher at the forefront of intelligent robotics and autonomous systems, with key contributions in deep reinforcement learning, path planning, and large language model (LLM) applications for robotics. His work bridges the gap between classical artificial intelligence challenges and modern AI-driven solutions. Hu’s most cited paper, “Target Tracking and Path Planning of Mobile Sensor Based on Deep Reinforcement Learning” (2023, 6 citations), addresses the limitations of traditional path planning algorithms—such as single-environment constraints and discrete action spaces—by introducing a reinforcement learning framework that enables adaptive, continuous decision-making in complex scenarios. This work has implications for defense, traffic, and robotics simulation. More recently, Hu pioneered “InspectionGPT: A Large Language Model-Based System for Inspection Task Planning” (2024, 3 citations), which leverages LLMs to overcome the cognitive and decision-making deficiencies of conventional inspection robots in dynamic environments. By integrating advanced reasoning into robotic task planning, this system marks a significant step toward more intelligent, autonomous inspection systems. With his innovative fusion of reinforcement learning and LLMs, Hu is shaping the future of mobile robotics and AI-driven automation.
Research Focus
Key Achievements
Top Papers
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