Robust and fast generation of top and side grasps for unknown objects
This work addresses the challenge of robust robotic grasping for unknown objects, which is incremental as it builds on geometry-based methods.
The paper tackles the problem of generating reliable top and side grasps for unknown objects using a single RGB-D camera, resulting in a sixfold increase in successful grasp attempts compared to a baseline method.
In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camera, and of selecting the most promising one. We demonstrate the effectiveness of our approach on a picking scenario on a real robot platform. Our approach has shown to be more reliable than another recent geometry-based method considered as baseline [7] in terms of grasp stability, by increasing the successful grasp attempts by a factor of six.