Mariam Elsayed
Papers
2
Total Citations
29
H-Index
2
About
Mariam Elsayed is a robotics researcher whose work lies at the intersection of computer vision, human-robot interaction, and autonomous manipulation. Her primary research areas include multi-modality 6DoF object tracking and adaptive motion planning for close-proximity human-robot collaboration. In her most-cited work, "Fusing Visual Appearance and Geometry for Multi-Modality 6DoF Object Tracking" (2023, 18 citations), Elsayed developed a novel tracking algorithm that integrates visual appearance with geometric data to achieve robust, continuous pose estimation—a critical capability for advanced robotic manipulation in dynamic environments. Her earlier foundational paper, "Adaptation and Transfer of Robot Motion Policies for Close Proximity Human-Robot Interaction" (2019, 11 citations), addresses the dual challenges of safety and comfort in collaborative settings. Here, she proposed adaptive motion policies that enable robots to reliably avoid dynamic obstacles like human arms while maintaining natural, comfortable trajectories. This work is particularly notable for balancing high-reliability obstacle avoidance with human-centric comfort metrics. Elsayed’s contributions are shaping safer, more intuitive human-robot collaboration, with her multi-modality tracking approach paving the way for more dexterous and context-aware robotic systems.
Research Focus
Key Achievements
Top Papers
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