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Imitating by generating: deep generative models for imitation of interactive tasks
[article]
2019
arXiv
pre-print
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner. They require the ability to predict and adapt to one's partner during an interaction. In this work we want to explore these ideas in a human-robot interaction setting in which a robot is required to learn interactive tasks from a combination of observational
arXiv:1910.06031v1
fatcat:alzh4zso6fe45mr6loawtjo6hu