Jeong-Ran Lee
Papers
1
Total Citations
6
H-Index
1
About
Jeong-Ran Lee is a robotics researcher whose work bridges the gap between reinforcement learning and real-world autonomous navigation. Her primary research areas include collision avoidance, local path planning, and the application of deep RL algorithms to physical robotic systems. Lee’s most notable contribution is the development of SACPlanner, a Soft Actor Critic-based local planner that uses polar state representations to achieve robust, real-world collision avoidance. Her 2023 paper on this topic demonstrates that enhancements to the SAC algorithm, such as RAD and DrQ, can achieve near-perfect training performance in as few as 10,000 episodes—a significant milestone for deploying RL in practical robotics. With 6 citations to date, this work has already influenced researchers seeking to move beyond simulation and into reliable, real-world deployment. Lee’s focus on training efficiency and trajectory quality marks her as a rising figure in the field, offering a promising path toward safer, more adaptive autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1