Abstract
This paper addresses the problem of robotic grasping and manipulation of 3D deformable objects, such as rubber balls or bags filled with sand. Specifically, we have developed a generalized learning algorithm for handling of 3D deformable objects in which prior knowledge of object attributes is not required and thus it can be applied to a large class of object types. Our methodology relies on the implementation of two main tasks: to calculate deformation characteristics for a non-rigid object represented by a physically-based model; and to calculate the minimum force required to successfully lift the deformable object. This minimum lifting force can be learned using a technique called 'iterative lifting'. Once the deformation characteristics and the associated lifting force term are determined, they are used to train a neural network for extracting the minimum force required for subsequent deformable object manipulation tasks. Our developed algorithm has been validated by experiments.
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