Neural End-to-End Learning of Reach for Grasp Ability with a 6-DoF Robot Arm
IROS 2018 Workshop on Machine Learning in Robot Motion Planning
- Sep 2018
Associated documents :
We present a neural end-to-end learning approach
for a reach-for-grasp task on an industrial UR5 arm. Our
approach combines the generation of suitable training samples
by classical inverse kinematics (IK) solvers in a simulation
environment in conjunction with real images taken from the
grasping setup. Samples are generated in a safe and reliable way
independent of real robotic hardware. The neural architecture
is based on a pre-trained VGG16 network and trained on our
collected images as input and motor joint values as output.
The approach is evaluated by testing the performance on two
test sets of different complexity levels. Based on our results, we
outline challenges and solutions when combining classical and
neural visuomotor approaches.
@InProceedings{BKGZEW18,
author = {Beik-Mohammadi, Hadi and Kerzel, Matthias and Görner, Michael and Zamani, Mohammad Ali and Eppe, Manfred and Wermter, Stefan},
title = {Neural End-to-End Learning of Reach for Grasp Ability with a 6-DoF Robot Arm},
booktitle = {IROS 2018 Workshop on Machine Learning in Robot Motion Planning},
number = {},
volume = {},
pages = {},
year = {2018},
month = {Sep},
publisher = {},
doi = {},
}