An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning

Abstract-- Deep learning is an established framework for learning hierarchical data representations. While compute power is in abundance, one of the main challenges in applying this framework to robotic grasping has been obtaining the amount of data needed to learn these representations, and structuring the data to the task at hand. Among contemporary approaches in the literature, we highlight key properties that have encouraged the use of deep learning techniques, and in this paper, detail our experience in developing a simulator for collecting cylindrical precision grasps of a multi-fingered dexterous robotic hand. Grasping and manipulation are important and challenging problems in Robotics. For grasp synthesis or pre-grasp planning, there are currently two dominant approaches: analytical and data-driven (i.e. Analytic approaches typically optimize some measure of force-or form-closure [22] [6], and provide guarantees on grasp properties such as: disturbance rejection, dexterity, equilibrium, and stability [23]. These models often require full knowledge of the object geometry, surface friction, and other intrinsic characteristics.