Monica Sileo
Papers
11
Total Citations
95
H-Index
5
About
Monica Sileo is a leading researcher at the intersection of robotics, computer vision, and human-robot interaction, with a focus on advancing autonomous assembly and collaborative manufacturing. Her work centers on solving fundamental challenges in industrial automation, particularly the precise “Peg-in-Hole” assembly task under uncertainty. By integrating deep learning—such as CNN-based hole detection and semantic image segmentation—with 3D surface reconstruction, she has developed robust vision systems that enable robots to mate automotive body parts with high accuracy, even when workpiece positions are unknown. Her most cited paper (31 citations) pioneers a method that combines 3D reconstruction and deep learning for autonomous assembly, while her subsequent work (12–14 citations) extends these techniques to real-world automotive scenarios. Sileo is also a pioneer in human-robot collaboration, creating an Augmented Reality toolkit for HoloLens 2 that allows non-expert operators to intuitively interact with collaborative robots (15 citations). Her broader impact includes vision-based robot-to-robot object handover and a novel application of social robotics for autism therapy, using a NAO robot for gaze-contingent eye tracking. With over 90 total citations across ten papers, Sileo’s contributions are shaping the future of intelligent, human-centric manufacturing.
Research Focus
Key Achievements
Top Papers
- 1Peg-in-Hole Using 3D Workpiece Reconstruction and CNN-based Hole Detection31 citations · 2020
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- 5Mixed Reality Platform Supporting Human-Robot Interaction6 citations · 2022
- 6Vision based robot-to-robot object handover5 citations · 2021
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- 10Grasping of Solid Industrial Objects Using 3D Registration2 citations · 2023