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.

早期研究旨在通过将人类动作领域中的关节位置序列转化为给定机器人能够实现且受其体现约束的动作领域，从而改进在线人机模仿技术。通过提出一个编码器 - 解码器神经网络模型进行领域转换，利用深度学习方法的泛化能力来解决这个问题。为了训练这样的模型，可以使用与机器人和人类动作相关联的配对数据，然而这样的数据在实践中非常稀少且收集费时。因此，我们转向了无配对领域转换的深度学习方法，并将其改进以实现人机模仿。