To teach robots skills, it is crucial to obtain data with supervision. Since
annotating real world data is time-consuming and expensive, enabling robots to
learn in a self-supervised way is important. In this work, we introduce a robot
system for self-supervised 6D object pose estimation. Starting from modules
trained in simulation, our system is able to label real world images with
accurate 6D object poses for self-supervised learning. In addition, the robot
interacts with objects in the environment to change the object configuration by
grasping or pushing objects. In this way, our system is able to continuously
collect data and improve its pose estimation modules. We show that the
self-supervised learning improves object segmentation and 6D pose estimation
performance, and consequently enables the system to grasp objects more
reliably. A video showing the experiments can be found at
https://youtu.be/W1Y0Mmh1Gd8.Comment: Accepted to International Conference on Robotics and Automation
(ICRA), 202