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author = {Yin, H. and Melo, F. S. and Billard, A. and Paiva, A.},
title = {New},
howpublished = {In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). San Francisco, California, USA.},
year = {2017},
abstract = {We contribute a learning from demonstration approach for
robots to acquire skills from multi-modal high-dimensional
data. Both latent representations and associations of differ-
ent modalities are proposed to be jointly learned through
an adapted variational auto-encoder. The implementation and
results are demonstrated in a robotic handwriting scenario,
where the visual sensory input and the arm joint writing mo-
tion are learned and coupled. We show the latent representa-
tions successfully construct a task manifold for the observed
sensor modalities. Moreover, the learned associations can be
exploited to directly synthesize arm joint handwriting motion
from an image input in an end-to-end manner. The advan-
tages of learning associative latent encodings are further high-
lighted with the examples of inferring upon incomplete input
images. A comparison with alternative methods demonstrates
the superiority of the present approach in these challenging

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