  This early-stage research work aims to improve online human-robot imitation
by translating sequences of joint positions from the domain of human motions to
a domain of motions achievable by a given robot, thus constrained by its
embodiment. Leveraging the generalization capabilities of deep learning
methods, we address this problem by proposing an encoder-decoder neural network
model performing domain-to-domain translation. In order to train such a model,
one could use pairs of associated robot and human motions. Though, such paired
data is extremely rare in practice, and tedious to collect. Therefore, we turn
towards deep learning methods for unpaired domain-to-domain translation, that
we adapt in order to perform human-robot imitation.
