4 papers · 1 filter
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity
Johann Huber, François Hélénon, Mathilde Kappel +5
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 t…
E2R: a Hierarchical-Learning inspired Novelty-Search method to generate diverse repertoires of grasping trajectories
Johann Huber, Oumar Sane, Alex Coninx +2
Robotics grasping refers to the task of making a robotic system pick an object by applying forces and torques on its surface. Despite the recent advances in data-driven approaches,…
Toward a Plug-and-Play Vision-Based Grasping Module for Robotics
François Hélénon, Johann Huber, Faïz Ben Amar +1
Despite recent advancements in AI for robotics, grasping remains a partially solved challenge, hindered by the lack of benchmarks and reproducibility constraints. This paper introd…
Speeding up 6-DoF Grasp Sampling with Quality-Diversity
Johann Huber, François Hélénon, Mathilde Kappel +4
Recent advances in AI have led to significant results in robotic learning, including natural language-conditioned planning and efficient optimization of controllers using generativ…