Enric Corona
Papers
5
Total Citations
291
H-Index
4
About
Enric Corona is a leading researcher at the intersection of computer vision, robotics, and human-machine interaction, with a core focus on understanding and predicting human hand-object interactions. His most influential work, "GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes" (2020, 170 citations), pioneered a novel approach to inferring how a human would grasp objects from a single RGB image—a critical step for applications in augmented reality and assistive robotics. Corona’s contributions extend to robotic manipulation, where he developed deep learning methods for active garment recognition and cloth classification using CNNs, enabling robots to perceive and handle deformable materials. His recent work on "Context-Aware Human Motion Prediction" (2020) advances the state-of-the-art in forecasting 3D skeletal motion by incorporating environmental context, while "Multi-FinGAN" (2021) tackles the challenging problem of generating collision-free grasps for multi-fingered robotic hands. With over 290 citations across his publications, Corona’s research bridges the gap between human dexterity and robotic capability, making him a key figure in the development of more intuitive and capable autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes170 citations · 2020
- 2
- 3Robot-Aided Cloth Classification Using Depth Information and CNNs23 citations · 2016
- 4Context-Aware Human Motion Prediction5 citations · 2020
- 5Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps3 citations · 2021