Huiyan Han

Papers

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About

Huiyan Han is a rising researcher in robotics and artificial intelligence, focusing on intelligent path planning for autonomous systems. Her work centers on leveraging deep reinforcement learning (DRL) to solve complex, nonlinear navigation challenges, particularly for lightweight robots. In her highly cited 2023 paper, "Improved Robot Path Planning Method Based on Deep Reinforcement Learning," Han advanced the DQN algorithm to enhance robot route efficiency, earning 26 citations and establishing a foundation for adaptive, real-time decision-making. She further innovated in 2024 with "Reinforcement learning path planning method incorporating multi-step Hindsight Experience Replay for lightweight robots," which improves sample efficiency and learning stability in sparse-reward environments. By integrating multi-step Hindsight Experience Replay, Han addresses critical limitations in traditional DRL, enabling robots to learn from past failures more effectively. Her contributions are pivotal for applications in autonomous vehicles, warehouse logistics, and service robotics, where efficient, safe navigation is paramount. With her work gaining traction in the robotics community, Huiyan Han is recognized for bridging theoretical DRL advances with practical, deployable solutions, making her a notable emerging voice in intelligent motion planning.