Seung-beom Jo
Papers
1
Total Citations
4
H-Index
1
About
Seung-beom Jo is a researcher focused on advancing autonomous navigation for mobile robots through deep reinforcement learning. His most-cited work, "Natural Behavior Learning Based on Deep Reinforcement Learning for Autonomous Navigation of Mobile Robots" (2018), introduces a novel method enabling two-wheeled mobile robots to navigate unknown environments using LiDAR sensors. By integrating deep learning with Q-learning theory—specifically through a deep Q-network (DQN)—Jo’s approach allows robots to learn natural, adaptive behaviors without pre-programmed paths. This contribution addresses a critical challenge in robotics: achieving robust, real-time navigation in dynamic, unstructured settings. With 4 citations, his work has laid groundwork for more intelligent and autonomous robotic systems. Jo’s research sits at the intersection of reinforcement learning, sensor-based perception, and mobile robotics, offering practical solutions for applications ranging from warehouse automation to search-and-rescue missions. His focus on end-to-end learning from raw sensor data highlights a shift toward data-driven, self-improving robots, making his contributions valuable for students and researchers exploring autonomous systems and embodied AI.
Research Focus
Key Achievements
Top Papers
- 1