Testing an unsupervised deep learning model for robot imitation of human motions

Robots that can closely imitate the actions and movements of humans in real-time could be incredibly useful, as they could learn to complete everyday tasks in specific ways without having to be extensively pre-programmed on these tasks. While techniques to enable imitation learning considerably improved over the past few years, their performance is often hampered by the lack of correspondence between a robot's body and that of its human user.
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