No abstract

Grasping is an essential prerequisite for an agent, either human or robotic, to manipulate various kinds of objects present in the world. It is a fact that we would like robots to have the same skills as we do. However, despite the construction of human-hand-like robotic effectors, much work is still to be done in order to give robots the capability to grasp and manipulate objects. The goal of this work is to automatically perform grasp synthesis of unknown planar objects. In other words, we must compute points on the object's boundary to be reached by the robotic fingers such that the resulting grasp, among infinite possibilities, optimizes some given criteria. The space of possible configurations is analyzed using genetic algorithms. However, genetic algorithms are not suitable to applications where time is a critical issue. In order to achieve real-time characteristics of the algorithm, neural networks are used: a huge training-set is collected off-line using genetic algorithms, and a feedforward network is trained on these values. We will demonstrate the usefulness of this approach in the process of grasp synthesis, and show the results achieved on an anthropomorphic arm/hand robot.