Recursive learning for deformable object manipulation

This paper presents a generalized approach to handling of 3D deformable objects. Our task is to learn robotic grasping characteristics for a non-rigid object represented by a physically-based model. The model is derived from discretizing the object into a network of interconnected particles and springs. Using Newtonian equations, we model the particle motion of a deformable object and thus calculate the deformation characteristics of the object. These deformation characteristics allow us to learn the required minimum forces necessary to successfully grasp the object and by linking these parameters into a learning table, we can subsequently retrieve the forces necessary to grasp an object presented to the system during run time. This new method of learning is presented and the results of a virtual simulation are shown.