4 citations · 5 across the 14 of their papers we have counts for
12 papers · 1 filter
Towards Robust Prehensile Manipulation in Open-Ended Environments
Mathilde Kappel, Mahdi Khoramshahi, Louis Annabi +2
We propose to use Quality-Diversity (QD) algorithms to solve robotic prehensile manipulation tasks in open-ended environments. Our approach enables the efficient discovery of a wid…
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…
Autonomously Acquiring Robot Manipulation Skills with Language-Driven Quality-Diversity
Émiland Garrabé, Mahdi Khoramshahi, Stéphane Doncieux
Quality-diversity (QD) algorithms have been gaining traction in robot learning, where diverse motion primitive libraries allow robots to adapt zero-shot to constraints at deploymen…
QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation
Mathilde Kappel, Mahdi Khoramshahi, Louis Annabi +2
Thanks to the latest advances in learning and robotics, domestic robots are beginning to enter homes, aiming to execute household chores autonomously. However, robots still struggl…
Placeit! A Framework for Learning Robot Object Placement Skills
Amina Ferrad, Johann Huber, François Hélénon +3
Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of…
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…
Enhancing Robustness in Language-Driven Robotics: A Modular Approach to Failure Reduction
Émiland Garrabé, Pierre Teixeira, Mahdi Khoramshahi +1
Recent advances in large language models (LLMs) have led to significant progress in robotics, enabling embodied agents to better understand and execute open-ended tasks. However, e…
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…