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Faïz Ben Amar

6 accepted 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 3Source ↗Scholar ↗
2025

Tactile-based force estimation for interaction control with robot fingers

Elie Chelly, Andrea Cherubini, Philippe Fraisse, Faïz Ben Amar, Mahdi Khoramshahi
IROS 2025

Fine dexterous manipulation requires reactive control based on rich sensing of manipulator-object interactions. Tactile sensing arrays provide rich contact information across the manipulator’s surface. However their implementation faces two main challenges: accurate force estimation across complex s

Cited by 2Source ↗Scholar ↗
2024

Closed-Loop Shape Control of Deformable Linear Objects Based on Cosserat Model

Artinian Azad, Faïz Ben Amar, Véronique Perdereau
RA-L 2024

The robotic shape control of deformable linear objects has garnered increasing interest within the robotics community. Despite recent progress, the majority of shape control approaches can be classified into two main groups: open-loop control, which relies on physically realistic models to represent

Cited by 7Source ↗Scholar ↗
2024

Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets

Johann Huber, François Hélénon, Hippolyte Watrelot, Faïz Ben Amar, Stéphane Doncieux
ICRA 2024poster

Robotic grasping refers to making a robotic system pick an object by applying forces and torques on its surface. Many recent studies use data-driven approaches to address grasping, but the sparse reward nature of this task made the learning process challenging to bootstrap. To avoid constraining the…

Cited by 16Source ↗code ↗Scholar ↗
2024

Speeding up 6-DoF Grasp Sampling with Quality-Diversity

Johann Huber, François Hélénon, Mathilde Kappel, Elie Chelly, Mahdi Khoramshahi, Faïz Ben Amar +1
IROS 2024poster

Recent advances in AI have led to significant results in robotic learning, including natural language-conditioned planning and efficient optimization of controllers using generative models. However, the interaction data remains the bottleneck for generalization. Getting data for grasping is a critic…

Cited by 3Source ↗Scholar ↗