Papers
5
Total Citations
135
H-Index
4
About
Antonio Gabas is a leading researcher in robotic manipulation, with a particular focus on the complex challenge of handling deformable objects such as clothing. His work sits at the intersection of computer vision, deep learning, and robotics, where he has pioneered methods for enabling robots to perceive, classify, and interact with non-rigid materials. Gabas’s most influential contribution is his 2017 paper on active garment recognition and target grasping point detection using deep learning, which has garnered 90 citations and established a foundational approach for robotic cloth handling. He further advanced the field by developing techniques for physical edge detection in clothing items, demonstrating that edges contain critical information for shape recognition and manipulation. His subsequent work on a dual edge classifier for robust cloth unfolding (2021) addresses the practical challenge of detecting how garments are folded, enabling more intelligent robotic actions. Gabas has also explored 6 DOF object pose estimation with minimal datasets, extending his expertise to rigid objects. Through his research, Gabas has significantly advanced the state of the art in robotic perception and manipulation of textiles, with direct applications in automated laundry, manufacturing, and service robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robot-Aided Cloth Classification Using Depth Information and CNNs23 citations · 2016
- 3Physical edge detection in clothing items for robotic manipulation12 citations · 2017
- 4Dual edge classifier for robust cloth unfolding6 citations · 2021
- 5Toward 6 DOF Object Pose Estimation with Minimum Dataset4 citations · 2019