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Self-Supervised Object-in-Gripper Segmentation from Robotic Motions

Boerdijk, Wout and Sundermeyer, Martin and Durner, Maximilian and Triebel, Rudolph (2020) Self-Supervised Object-in-Gripper Segmentation from Robotic Motions. In: Proceedings of Machine Learning Research. CoRL 2020, 16.-18. Nov. 2020, Virtual.

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Abstract

Accurate object segmentation is a crucial task in the context of robotic manipulation. However, creating sufficient annotated training data for neural networks is particularly time consuming and often requires manual labeling. To this end, we propose a simple, yet robust solution for learning to segment unknown objects grasped by a robot. Specifically, we exploit motion and temporal cues in RGB video sequences. Using optical flow estimation we first learn to predict segmentation masks of our given manipulator. Then, these annotations are used in combination with motion cues to automatically distinguish between background, manipulator and unknown, grasped object. In contrast to existing systems our approach is fully self-supervised and independent of precise camera calibration, 3D models or potentially imperfect depth data. We perform a thorough comparison with alternative baselines and approaches from literature. The object masks and views are shown to be suitable training data for segmentation networks that generalize to novel environments and also allow for watertight 3D reconstruction.

Item URL in elib:https://elib.dlr.de/139332/
Document Type:Conference or Workshop Item (Other)
Title:Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Boerdijk, WoutWout.Boerdijk (at) dlr.dehttps://orcid.org/0000-0003-0789-5970
Sundermeyer, Martinmartin.sundermeyer (at) dlr.dehttps://orcid.org/0000-0003-0587-9643
Durner, MaximilianMaximilian.Durner (at) dlr.dehttps://orcid.org/0000-0001-8885-5334
Triebel, RudolphRudolph.Triebel (at) dlr.dehttps://orcid.org/0000-0002-7975-036X
Date:2020
Journal or Publication Title:Proceedings of Machine Learning Research
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Self-Supervised Learning, Object Segmentation
Event Title:CoRL 2020
Event Location:Virtual
Event Type:international Conference
Event Dates:16.-18. Nov. 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Boerdijk, Wout
Deposited On:08 Dec 2020 14:52
Last Modified:08 Dec 2020 14:52

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