1 citations · 1 across the 5 of their papers we have counts for
6 papers
Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy, Stochastic or Uncertain Domains
Manon Flageat, Johann Huber, François Helenon +2
Quality-Diversity (QD) has demonstrated potential in discovering collections of diverse solutions to optimisation problems. Originally designed for deterministic environments, QD h…
Task-Aware Robotic Grasping by evaluating Quality Diversity Solutions through Foundation Models
Aurel X. Appius, Emiland Garrabe, Francois Helenon +3
Task-aware robotic grasping is a challenging problem that requires the integration of semantic understanding and geometric reasoning. This paper proposes a novel framework that lev…
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,…
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…