Yeon-ho Jo

About

Yeon-ho Jo is a researcher at the forefront of autonomous robotics and intelligent navigation systems, with a focus on integrating deep reinforcement learning into real-world robotic platforms. His most cited work, "Deep Reinforcement Learning-based ROS-Controlled RC Car for Autonomous Path Exploration in the Unknown Environment" (2020, 13 citations), presents a pioneering approach to training a LiDAR-equipped RC car within the GAZEBO simulation environment. By employing reshaped LiDAR data as input for a deep reinforcement learning agent, Jo demonstrated how low-cost hardware can achieve robust autonomous path exploration in unknown environments—a critical challenge in field robotics. This work bridges the gap between simulation-based training and real-world deployment, offering a scalable framework for robot navigation and obstacle avoidance. Jo’s contributions are particularly notable for their practical implementation using the Robot Operating System (ROS), making his methods accessible to researchers and students alike. His research continues to push the boundaries of how autonomous systems learn and adapt, with implications for search-and-rescue, industrial automation, and mobile robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Deep Reinforcement Learning-based ROS-Controlled RC Car for Autonomous Path Exploration in the Unknown Environment

   13 citations · 2020

Key Collaborators

Contact & Links