Intelligent Learning for Deformable Object Manipulation

The majority of manipulation systems are designed with the assumption that the objects being handled are rigid and do not deform when grasped. This paper addresses the problem of robotic grasping and manipulation of 3-D deformable objects, such as rubber balls or bags filled with sand. Specifically, we have developed a generalized learning algorithm for handling of 3-D 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. Our first task is to calculate deformation characteristics for a non-rigid object represented by a physically-based model. Using nonlinear partial differential equations, we model the particle motion of the deformable object in order to calculate the deformation characteristics. For our second task, we must 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 is validated with two sets of experiments. The first experimental results are derived from the implementation of the algorithm in a simulated environment. The second set involves a physical implementation of the technique whose outcome is compared with the simulation results to test the real world validity of the developed methodology.