5 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…
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…
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets
Johann Huber, François Hélénon, Hippolyte Watrelot +2
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,…
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…
Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics
J. Huber, F. Hélénon, M. Coninx +2
Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and high-performing solutions to a given problem. Originally developed for evolutionary robotics…