TY - GEN
T1 - Self-supervised 6D Object Pose Estimation for Robot Manipulation
AU - Deng, Xinke
AU - Xiang, Yu
AU - Mousavian, Arsalan
AU - Eppner, Clemens
AU - Bretl, Timothy
AU - Fox, Dieter
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - To teach robots skills, it is crucial to obtain data with supervision. Since annotating real world data is timeconsuming 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.
AB - To teach robots skills, it is crucial to obtain data with supervision. Since annotating real world data is timeconsuming 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.
UR - http://www.scopus.com/inward/record.url?scp=85089578530&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089578530&partnerID=8YFLogxK
U2 - 10.1109/ICRA40945.2020.9196714
DO - 10.1109/ICRA40945.2020.9196714
M3 - Conference contribution
AN - SCOPUS:85089578530
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3665
EP - 3671
BT - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Y2 - 31 May 2020 through 31 August 2020
ER -