Acquiring visual servoing reaching and grasping skills using neural reinforcement learning

In this work we present a reinforcement learning system for autonomous reaching and grasping using visual servoing with a robotic arm. Control is realized in a visual feedback control loop, making it both reactive and robust to noise. The controller is learned from scratch by success or failure without adding information about the task's solution. All of the system's major components are implemented as neural networks. The system is applied to solving a combined reaching and grasping task involving uncertainty directly on a real robotic platform. Its main parts and the conditions for their successful interoperation are described. It will be shown that even with minimal prior knowledge, the system can learn in a short amount of time to reliably perform its task. Furthermore, we describe the control system's ability to react to changes and errors.