March 13, 2024, 4:44 a.m. | Fran\c{c}ois H\'el\'enon, Johann Huber, Fa\"iz Ben Amar, St\'ephane Doncieux

cs.LG updates on

arXiv:2310.04349v2 Announce Type: replace-cross
Abstract: Despite recent advancements in AI for robotics, grasping remains a partially solved challenge, hindered by the lack of benchmarks and reproducibility constraints. This paper introduces a vision-based grasping framework that can easily be transferred across multiple manipulators. Leveraging Quality-Diversity (QD) algorithms, the framework generates diverse repertoires of open-loop grasping trajectories, enhancing adaptability while maintaining a diversity of grasps. This framework addresses two main issues: the lack of an off-the-shelf vision module for detecting object pose …

abstract algorithms arxiv benchmarks challenge constraints cs.lg diverse diversity framework grasping multiple paper quality reproducibility robotics type vision

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