Teaching a Robot Pick and Place Task using Recurrent Neural Network

Abstract

Programming a robot to perform a specific task is generally time consuming. This paper proposes a novel method to teach new task to a robot. The main contribution is the idea of building a task planner based on a Recurrent Neural Network (RNN). The neural network learns how to plan a task from observing a task sequence generated from a general motion planner. The method is evaluated by teaching pick and place tasks to a Baxter robot. The experiences are performed in a physical simulator. It shows that the robot can adapt to pick and place an object in various initial positions.