Max Robotics — Global Service Robot Directory + US Certification

About

Ayah Ahmad is a robotics researcher whose work focuses on enabling robots to manipulate complex, real-world objects through perception and planning. Her key research areas include deformable object manipulation, semantic scene understanding, and efficient multi-object grasping. She has made significant contributions to these fields, with her most cited papers collectively garnering over 16 citations. Notably, her work on "Bagging by Learning to Singulate Layers Using Interactive Perception" (2023, 6 citations) introduces a novel method for handling fabric and 2D deformable materials using only visual observations, a task critical for household and industrial applications. In "Lifelong LERF: Local 3D Semantic Inventory Monitoring Using FogROS2" (2024, 5 citations), she developed a method enabling mobile robots with minimal compute to maintain dense language and geometric representations for inventory monitoring in dynamic environments. Her "Busboy Problem" (2023, 5 citations) addresses efficient tableware decluttering by proposing policies for multi-object grasps, introducing the metric of Objects per Trip (OpT) to measure efficiency. Ahmad's work stands out for its practical approach to everyday robotic challenges, combining perception, learning, and efficient manipulation strategies.