Self-supervised 6D Object Pose Estimation for Robot Manipulation
This work addresses the challenge of data annotation for robot learning, offering a self-supervised approach that is incremental in automating pose estimation for manipulation tasks.
The paper tackles the problem of expensive real-world data annotation for robot manipulation by introducing a self-supervised system for 6D object pose estimation, which improves object segmentation and pose estimation performance, leading to more reliable object grasping.
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.