Mohammed Asfour
Papers
1
Total Citations
10
H-Index
1
About
Mohammed Asfour is a rising researcher in the field of robotic manipulation, with a core focus on tactile sensing and dexterous object handling. His work addresses a critical challenge in robotics: estimating the pose of objects held in a robotic hand, especially when visual data is obstructed. Asfour’s key contribution lies in pioneering the use of tactile temporal features—dynamic patterns of touch over time—to infer an object’s orientation during manipulation. This approach offers a robust alternative to traditional camera-based methods, which often fail under occlusion. His most-cited paper, “Exploring Tactile Temporal Features for Object Pose Estimation during Robotic Manipulation” (2023), has already garnered 10 citations, signaling its early impact in a rapidly evolving field. By advancing tactile-based state estimation, Asfour is helping to enable more reliable, adaptive robotic hands for tasks ranging from assembly to assistive care. His work sits at the intersection of sensorimotor control and machine learning, promising to deepen robots’ physical intelligence. As a young investigator, Asfour is establishing a reputation for innovative, problem-driven research that pushes the boundaries of how robots perceive and interact with the world through touch.
Research Focus
Key Achievements
Top Papers
- 1