Papers

3

Total Citations

14

H-Index

3

About

Ali Ridho Barakbah is a leading figure in intelligent robotics and computer vision, whose work bridges the gap between autonomous systems and real-world humanitarian needs. His research centers on developing advanced perception and control systems for mobile and humanoid robots, with a particular emphasis on creating practical, efficient solutions. A standout contribution is his "Walking Gait Learning for T-FLoW Humanoid Robot," where he pioneered a rule-based learning method that dramatically accelerates stable gait acquisition—a significant improvement over traditional, time-intensive reinforcement learning. In the realm of 3D computer vision, his "FLoW-Vision" system elevates object recognition and pose estimation from basic 2D to sophisticated 3D perception, enabling robots to replicate more human-like visual skills. Demonstrating the societal impact of his work, Barakbah developed a navigation system for holonomic mobile robots designed to deliver logistics to COVID-19 patients, directly reducing contact risk for healthcare workers. With his most-cited papers each garnering 4-5 citations, Barakbah’s research is not only technically innovative but also profoundly applied, showcasing how robotics can serve critical public health and operational challenges.

Research Focus

Key Achievements

3
H-Index
3
Papers
14
Total Citations
5
Avg Citations/Paper
🏆 Most Cited Paper
Walking Gait Learning for “T-FLoW” Humanoid Robot Using Rule-Based Learning
5 citations · 2021
📈 Most Prolific Year: 2021 (3 Papers)
🤝 Key Collaborators: 9
🏛 Institutions: Universitas Negeri Surabaya, Politeknik Elektronika Negeri Surabaya

Top Papers

  1. 1
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  3. 3

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 12 days ago