910 research outputs found
Self-supervised 6D Object Pose Estimation for Robot Manipulation
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
Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments with Minimal User Interaction
Recently developed deep neural networks achieved state-of-the-art results in
the subject of 6D object pose estimation for robot manipulation. However, those
supervised deep learning methods require expensive annotated training data.
Current methods for reducing those costs frequently use synthetic data from
simulations, but rely on expert knowledge and suffer from the "domain gap" when
shifting to the real world. Here, we present a proof of concept for a novel
approach of autonomously 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. We evaluate our autonomous data generation approach in two grasping
experiments, where we archive a similar grasping success rate as related work
on a non autonomously generated data set.Comment: This is a preprint and currently under peer review at IROS 202
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