@techreport { yin18,
	abstract = {In this paper, we learn dynamics from highdimensional
demonstrations to facilitate model-based prediction
and robot control. The proposed approach leverages the
progress in variational-bayes and sequence modeling, extracting
a low-dimensional latent space so the dynamical relations of
interest can be compactly represented and learned. Different
from existing works, our model captures latent dynamics
in a more general form and features efficient inference for
pattern filtering, prediction and synthesis. The extracted feature
mapping and latent dynamics can be naturally integrated in
robot learning, yielding task imitation from raw data and
prediction-based reproduction. The performance of latent dynamics
learning and model-based imitation is shown in three
tasks: 1) reconstructing and predicting images of bouncing balls
movement with an accuracy competitive to the state-of-the art;
2) synthesizing diverse handwriting image sequences; 3)
learning to strike a ball under partial visual input, with results
significantly outperforming baselines.},
	institution = {Intelligent Agents and Synthetic Characters Group},
	keywords = {Miscellaneous;Neural Computation;},
	month = {May},
	number = {GAIPS-TR-001-17},
	title = {Learning Variational Latent Dynamics: Towards Model-based Imitation and Control},
	year = {2018},
	author = {Hang Yin and Francisco S. Melo and Aude Billard and Ana Paiva}
}