Intelligent learning for deformable object manipulation

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