Abdullah Al-Zabt
Papers
1
Total Citations
2
H-Index
1
About
Abdullah Al-Zabt is a researcher in robotics and artificial intelligence, with a focus on deep reinforcement learning (DRL) and its application to robotic control systems. His most-cited work, "Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning" (2019), introduces a novel technique that leverages image-based feedback to train robotic arms using DRL. In this study, Al-Zabt employs an Actor-Critic agent with Temporal-Difference (TD) learning, enabling the robot to autonomously learn and adapt to its environment through visual cues. This approach advances the integration of computer vision and reinforcement learning, offering a pathway toward more intuitive and adaptive robotic manipulation. While his citation count is currently modest, his work contributes to foundational methods in embodied AI and autonomous systems. Al-Zabt’s research is particularly relevant for students and engineers exploring how robots can learn complex tasks from raw sensory data, bridging the gap between perception and action in real-world applications.
Research Focus
Key Achievements
Top Papers
- 1