7 papers · 1 filter
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…
Enhancing Robustness in Language-Driven Robotics: A Modular Approach to Failure Reduction
Ãmiland 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…
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…
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…