Roan Van Hoa
Papers
4
Total Citations
31
H-Index
3
About
Roan Van Hoa is a robotics and artificial intelligence researcher whose work sits at the intersection of autonomous systems, machine learning, and robotic control. His research primarily focuses on mobile robot navigation, reinforcement learning, and robotic arm simulation, with a particular emphasis on bridging theoretical AI methodologies with real-world industrial and autonomous applications. Among his most significant contributions is his pioneering application of deep reinforcement learning to mobile robot navigation in unknown environments, exploring how classic and deep RL algorithms can be scaled to handle complex, real-world state spaces — a challenge central to practical autonomous systems. His 2020 paper on reinforcement learning-based autonomous navigation has garnered 12 citations, while his robotic arm simulation work using MATLAB and the Robotics Toolbox has become a useful reference for industry-oriented robotics education, accumulating 14 citations. More recently, he has advanced the application of Deep Deterministic Policy Gradient (DDPG) algorithms tested both in Gazebo simulation and physical robot platforms, demonstrating a commitment to translatable, deployable AI solutions. With a growing body of work and a cumulative citation record reflecting rising recognition in the field, Roan Van Hoa represents an emerging voice in intelligent robotics research particularly relevant to students exploring autonomous systems and applied machine learning.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4