Yaokun Tang
Papers
1
Total Citations
2
H-Index
1
About
Yaokun Tang is a researcher advancing the frontiers of autonomous robotics through intelligent control systems. His primary focus lies in developing robust obstacle avoidance methodologies for mobile robots, with a particular emphasis on integrating deep reinforcement learning to enhance real-time navigation in complex environments. Tang’s most cited work, "Robot Obstacle Avoidance Controller Based on Deep Reinforcement Learning" (2022), addresses a critical limitation in traditional path planning and guidance-based approaches: their poor performance and low efficiency in unpredictable, complicated settings. By proposing a learning-driven controller, he offers a more adaptive solution that improves the stability and responsiveness of robotic systems during autonomous operation. While still early in his career, with 2 citations on this foundational paper, Tang’s contribution is notable for tackling a persistent bottleneck in mobile robotics—bridging the gap between theoretical control methods and practical deployment in dynamic, real-world scenarios. His work signals a promising trajectory in reinforcement learning-based autonomy, with potential applications spanning service robots, autonomous vehicles, and industrial automation.
Research Focus
Key Achievements
Top Papers
- 1Robot Obstacle Avoidance Controller Based on Deep Reinforcement Learning2 citations · 2022