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About

Jiaqi Wang is an emerging researcher at the intersection of robotics, artificial intelligence, and autonomous systems, with a particular focus on path planning and the integration of large language models into robotic applications. Wang's most influential contribution, "Improved Robot Path Planning Method Based on Deep Reinforcement Learning" (2023), has garnered 26 citations and advances the application of Deep Q-Network (DQN) algorithms to tackle the inherently nonlinear challenges of robotic navigation. Building on this foundation, Wang has explored the transformative potential of large language models in robotics, with a 2024 survey on opportunities and challenges in LLM-driven robot task planning already accumulating 20 citations — a remarkable reception for such a recent work. Further demonstrating a commitment to practical deployment, Wang's research on multi-step Hindsight Experience Replay refines reinforcement learning strategies specifically for lightweight robotic platforms. Together, these contributions reflect a coherent research vision: making autonomous robots smarter, more adaptable, and more capable of reasoning in complex environments. Wang's work is positioned at a timely convergence of deep learning and embodied intelligence, making it highly relevant for students and researchers pursuing the next generation of intelligent robotic systems.