Towards Robot-Assisted Data Generation with Minimal User Interaction for Autonomously Training 6D Pose Estimation in Operational Environments

Abstract

Recently, deep neural networks achieved state-of-the-art results in the subject of 6D object pose estimation for robot manipulation. However, those deep learning methods require expensive training data. Current methods for cost reduction use synthetic data, but rely on expert knowledge and suffer from the domain gap when shifting to the real application. Here, we present a proof of concept for a novel approach towards generating annotated training data for 6D object pose estimation. This approach is designed for learning new objects in operational environments while requiring little interaction and no expertise on the part of the user.