Injoon Min
Papers
1
Total Citations
5
H-Index
1
About
Injoon Min is a robotics researcher focused on advancing bipedal locomotion for humanoid robots, with a particular emphasis on reinforcement learning techniques. His most-cited work, "DDPG Reinforcement Learning Experiment for Improving the Stability of Bipedal Walking of Humanoid Robots" (2023, 5 citations), introduces a novel approach to enhancing walking stability by applying deep deterministic policy gradient (DDPG) algorithms in real-world environments. Min developed a treadmill-like testbed where trajectory parameters are dynamically optimized through reinforcement learning, addressing the critical challenge of maintaining balance during bipedal motion. This work bridges the gap between simulation-based control and practical robotic deployment, offering a scalable method for adaptive gait generation. By demonstrating that DDPG can effectively stabilize humanoid walking under real-world conditions, Min contributes to the broader goal of creating more agile and reliable humanoid robots for applications in disaster response, healthcare, and human-robot interaction. His research represents a significant step toward autonomous locomotion systems that can learn and adapt to complex terrains without extensive manual programming.
Research Focus
Key Achievements
Top Papers
- 1