10 citations · 11 across the 4 of their papers we have counts for
4 papers
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…
Closed-loop shape control of deformable linear objects based on Cosserat model
Azad Artinian, Faiz Ben Amar, Veronique Perdereau
The robotic shape control of deformable linear objects has garnered increasing interest within the robotics community. Despite recent progress, the majority of shape control approa…
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…