Pablo Gil — accepted AI-conference papers

2025

Qdgset: a Large Scale Grasping Dataset Generated With Quality-Diversity

Johann Huber, François Hélénon, Mathilde Kappel, Ignacio de Loyola Páez-Ubieta, Santiago T. Puente, Pablo Gil +2

ICRA 2025

Recent advances in AI have led to significant results in robotic learning, but skills like grasping remain partially solved. Many recent works exploit synthetic grasping datasets to learn to grasp unknown objects. However, those datasets were generated using simple grasp sampling methods using prior

Cited by 3 Source ↗Scholar ↗

2023

Measuring Object Rotation via Visuo-Tactile Segmentation of Grasping Region

Julio Castaño-Amoros, Pablo Gil

RA-L 2023

When carrying out robotic manipulation tasks, objects occasionally fall as a result of the rotation caused by slippage. This can be prevented by obtaining tactile information that provides better knowledge on the physical properties of the grasping. In this letter, we estimate the rotation angle of

Cited by 11 Source ↗Scholar ↗

2022

Editorial: Robotic Handling of Deformable Objects

Jihong Zhu, Claire Dune, Miguel Aranda, Youcef Mezouar, Juan Antonio Corrales, Pablo Gil +1

RA-L 2022

The papers in this special section focus on robotic handling of deformable objects. Object handling and manipulation is a recurrent theme in robotics, moving progressively from simple rigid objects to articulated objects and then complex deformable objects. In recent years, there has been a growing

Cited by 4 Source ↗Scholar ↗