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Found 3 papers, 2 papers with code
Speeding up 6-DoF Grasp Sampling with Quality-Diversity
We believe these results to be a significant step toward the generation of large datasets that can lead to robust and generalizing robotic grasping policies.
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets
More than 7000 reach-and-grasp trajectories have been generated with Quality-Diversity (QD) methods on 3 different arms and grippers, including parallel fingers and a dexterous hand, and tested in the real world.
Toward a Plug-and-Play Vision-Based Grasping Module for Robotics
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.
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