  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.
