1 citations · 1 across the 5 of their papers we have counts for
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A Sim-to-Real Integration Pipeline for Training and Deployment of Chunk-Based VLA Manipulation Policies
Mathilde Kappel, Clémence Grislain, Mohamed Chetouani +5
Vision-Language-Action (VLA) models have become a prominent paradigm for mapping multimodal inputs, including semantic instructions, visual observations of the scene, and proprioce…
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…
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,…