Hendawan Soebakti
Papers
1
Total Citations
31
H-Index
1
About
Hendawan Soebakti is a researcher at the forefront of integrating deep learning with robotics, particularly in the domain of autonomous humanoid robot soccer. His primary research areas encompass computer vision, object detection, and real-time robotic perception. Soebakti’s most notable contribution is his pioneering work on applying the You Only Look Once (YOLO) deep learning framework for real-time ball and goal detection in the Barelang-FC humanoid robot system. This 2017 study, which has garnered 31 citations, addressed a critical challenge in robotics: enabling a robot to rapidly and accurately identify dynamic objects in a competitive environment. By demonstrating that YOLO could be effectively deployed for this task, Soebakti helped bridge the gap between state-of-the-art object detection algorithms and practical robotic applications. His work is significant not only for advancing the capabilities of soccer-playing robots but also for providing a scalable approach to real-time vision systems in other autonomous platforms. Soebakti’s research continues to inspire developments in embedded AI and vision-guided robotics, making him a key figure in the intersection of deep learning and robotic perception.
Research Focus
Key Achievements
Top Papers
- 1