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Çetin Meriçli, Manuela Veloso, and H. Levent Akın. Improving Biped Walk Stability with Complementary Corrective Demonstration. Autonomous Robots, 32(4):419–432, Springer Netherlands, 2012. 10.1007/s10514-012-9284-1
We contribute a method for improving the skill execution performance of a robot by complementing an existing algorithmic solution with corrective human demonstration. We apply the proposed method to the biped walking problem, which is a good example of a complex low level skill due to the complicated dynamics of the walk process in a high dimensional state and action space. We introduce an incremental learning approach to improve the Nao humanoid robot’s stability during walking. First, we identify, extract, and record a complete walk cycle from the motion of the robot as it executes a given walk algorithm as a black box. Second, we apply offline advice operators for improving the stability of the learned open-loop walk cycle. Finally, we present an algorithm to directly modify the recorded walk cycle using real time corrective human demonstration. The demonstrator delivers the corrective feedback using a commercially available wireless game controller without touching the robot. Through the proposed algorithm, the robot learns a closed-loop correction policy for the open-loop walk by mapping the corrective demonstrations to the sensory readings received while walking. Experiment results demonstrate a significant improvement in the walk stability.
@article{mericli2012b,
author = {Çetin Meriçli and Manuela Veloso and H. Levent Akın},
title = {Improving Biped Walk Stability with Complementary Corrective Demonstration},
journal = {Autonomous Robots},
publisher = {Springer Netherlands},
year = {2012}
issn = {0929-5593},
keyword = {Computer Science},
pages = {419--432},
volume = {32},
number = {4},
url = {http://dx.doi.org/10.1007/s10514-012-9284-1},
note = {10.1007/s10514-012-9284-1},
abstract = {We contribute a method for improving the skill execution performance of a robot by complementing an existing algorithmic solution with corrective human demonstration. We apply the proposed method to the biped walking problem, which is a good example of a complex low level skill due to the complicated dynamics of the walk process in a high dimensional state and action space. We introduce an incremental learning approach to improve the Nao humanoid robot’s stability during walking. First, we identify, extract, and record a complete walk cycle from the motion of the robot as it executes a given walk algorithm as a black box. Second, we apply offline advice operators for improving the stability of the learned open-loop walk cycle. Finally, we present an algorithm to directly modify the recorded walk cycle using real time corrective human demonstration. The demonstrator delivers the corrective feedback using a commercially available wireless game controller without touching the robot. Through the
proposed algorithm, the robot learns a closed-loop correction policy for the open-loop walk by mapping the corrective demonstrations to the sensory readings received while walking. Experiment results demonstrate a significant improvement in the walk stability.},
bib2html_pubtype = {Journal Article},
bib2html_rescat = {Learning from Demonstration},
bib2html_dl_pdf = {../files/cmericliAR2011ImprovingBipedWalk.pdf},
}
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