ロボットマニピュレーションのための自己教師あり6D物体姿勢推定
Self-supervised 6D Object Pose Estimation for Robot Manipulation
シミュレーションで学習したモデルを起点に、実世界画像へ正確な6D姿勢ラベルを自動付与し、物体の把持・押し動作でデータを継続収集しながら姿勢推定を自己教師ありで改善するロボットシステムを提案した。
著者: Xinke Deng, Yu Xiang, Arsalan Mousavian, Clemens Eppner, Timothy Bretl, Dieter Fox
分類: cs.RO
原文アブストラクト
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.
