Domen Tabernik

Papers

Total Citations

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About

Domen Tabernik is a leading researcher in computer vision and robotics, with a primary focus on advancing robotic manipulation through deep learning. His most influential work centers on the challenge of object grasping, particularly for deformable and non-rigid materials like fabrics and cloths. Tabernik introduced the Center Direction Network (CeDiRNet-3DoF), a groundbreaking deep-learning model that significantly improves grasping point localization on cloths, addressing a fundamental hurdle in robotic manipulation. This work, published in 2024, has already garnered 12 citations, underscoring its immediate impact on the field. Earlier in his career, Tabernik contributed to spatial AI for mobile robotics, proposing a novel compositional hierarchical representation for room categorization. This 2013 study, with 7 citations, demonstrated his early ability to tackle complex spatial modeling problems. Tabernik's research elegantly bridges theoretical advances in deep learning with practical robotics applications, making him a key figure in enabling robots to handle everyday deformable objects—a critical step toward more capable and autonomous robotic systems in real-world environments.