Automatic pre-grasps generation for unknown 3D objects
This addresses the challenge of robotic grasping for novel objects, but it appears incremental as it builds on existing decomposition and grasping methods.
The paper tackles the problem of automatically generating pre-grasps for unknown 3D objects by decomposing them into parts and using a tree structure with box primitives, achieving implementation on 24 household objects and toys.
In this paper, the problem of automating the pre-grasps generation for novel 3d objects has been discussed. The objects represented as cloud of 3D points are split into parts and organized in a tree structure, where parts are approximated by simple box primitives. Applying grasping only on the individual object parts may miss a good grasp which involves a combination of parts. The problem has been addressed by traversing the decomposition tree and checking each node of the tree for possible pre-grasps against a set of conditions. Further, a face mask has been introduced to encode the free and blocked faces of the box primitives. Pre-grasps are generated only for the free faces. Finally, the proposed method implemented on a set twenty-four household objects and toys, where a grasp planner based on object slicing method has been used to compute the contact-level grasp plan.