Xie Han

North University of China, Shanxi University

Papers

Total Citations

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About

Xie Han is a rising researcher at the forefront of intelligent robotics, specializing in path planning, deep reinforcement learning (DRL), and robotic manipulation. Their work addresses the fundamental challenge of enabling robots to navigate and interact with complex, unstructured environments. Han’s most impactful contribution is a novel DRL-based path planning method that leverages an improved Deep Q-Network (DQN) algorithm, published in 2023 and already garnering 26 citations. This work demonstrates how DRL can solve the nonlinear path planning problem more effectively than traditional approaches. Building on this, Han introduced a multi-step Hindsight Experience Replay technique for lightweight robots, significantly enhancing sample efficiency in sparse-reward settings. In parallel, their research on 6-DoF grasp pose estimation using instance reconstruction tackles the critical problem of robotic grasping, achieving precise pose prediction from partial visual data. With a growing citation footprint and a clear trajectory toward practical, learning-based solutions, Xie Han is establishing themselves as a key contributor to the next generation of autonomous robotic systems.