Arafat Al-Dhaqm
Papers
2
Total Citations
55
H-Index
2
About
Arafat Al-Dhaqm is a leading researcher in the field of robotic manipulation, with a specific focus on enabling robots to operate intelligently in cluttered and visually complex environments. His work sits at the intersection of computer vision, deep reinforcement learning, and dexterous robotics. Al-Dhaqm’s major contributions include a comprehensive review of learning-based manipulation strategies, which has become a key reference for the field, accumulating 39 citations. He has also pioneered novel approaches to deep reinforcement learning for robotic grasping, directly addressing the critical challenges of clutter and occlusion—situations where target objects are partially hidden or surrounded by other items. His 2021 paper on this topic, with 16 citations, emphasizes the importance of spatial equivariance in visual perception, a concept that allows robots to maintain robust grasping policies even when objects are in varying positions or orientations. By tackling these fundamental obstacles, Al-Dhaqm’s research is paving the way for more autonomous and reliable robots capable of assisting humans in dangerous or difficult tasks, from industrial sorting to domestic assistance.
Research Focus
Key Achievements
Top Papers
- 1Review of Learning-Based Robotic Manipulation in Cluttered Environments39 citations · 2022
- 2Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion16 citations · 2021