Representations for object grasping and learning from experience

We study two important problems in the area of robot grasping: i) the methodology and representations for grasp selection on known and unknown objects, and ii) learning from experience for grasping of similar objects. The core part of the paper is the study of different representations necessary for implementing grasping tasks on objects of different complexity. We show how to select a grasp satisfying force-closure, taking into account the parameters of the robot hand and collision-free paths. Our implementation takes also into account efficient computation at different levels of the system regarding representation, description and grasp hypotheses generation.

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