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 and the generalization of QD trajectories to the whole robot operational space. The proposed solution combines multiple vision modules for 6DoF object detection and tracking while rigidly transforming QD-generated trajectories into the object frame. Experiments on a Franka Research 3 arm and a UR5 arm with a SIH Schunk hand demonstrate comparable performance when the real scene aligns with the simulation used for grasp generation. This work represents a significant stride toward building a reliable vision-based grasping module transferable to new platforms, while being adaptable to diverse scenarios without further training iterations.

翻译：尽管人工智能在机器人领域取得了最新进展，但抓取问题仍因缺乏基准测试和可复现性限制而部分未获解决。本文提出了一种可轻松迁移至多个操作器的视觉抓取框架。该框架利用质量多样性算法生成多样化的开环抓取轨迹库，在保持抓取多样性的同时增强适应性。该框架主要解决两个问题：缺乏用于检测物体姿态的现成视觉模块，以及将质量多样性轨迹泛化至整个机器人操作空间。所提出的方案结合了多个用于六自由度物体检测与跟踪的视觉模块，同时将质量多样性生成轨迹刚性变换至物体坐标系。在配备SIH Schunk手爪的Franka Research 3机械臂和UR5机械臂上进行的实验表明，当真实场景与用于生成抓取的仿真环境一致时，取得了可比的性能。这项工作标志着向构建可迁移至新平台、无需额外训练迭代即可适应多样化场景的可靠视觉抓取模块迈出了重要一步。