Minjae Park

Papers

Total Citations

H-Index

About

Minjae Park is a robotics researcher specializing in autonomous navigation and human-robot interaction, with a focus on reinforcement learning for mobile robots operating in complex environments. His most significant contribution is the development of a deep deterministic policy gradient (DDPG)-based path-planning method that integrates hindsight experience replay (HER) to address sparse reward challenges in autonomous driving. This work, published in 2022 and cited 21 times, provides a robust solution for mobile robots to learn effective navigation policies even when feedback is limited, advancing the field of autonomous driving in real-world settings. Park also explores user motion recognition for robot arm control using the Robot Operating System (ROS), demonstrating his interest in intuitive human-robot interfaces. His research bridges theoretical reinforcement learning algorithms with practical robotic applications, offering valuable insights for students and researchers working on autonomous systems. With a growing citation record, Park’s work is gaining recognition for its potential to enhance robot autonomy in sparse reward environments, a critical challenge in robotics.