Time-Contrastive Networks: Self-Supervised Learning from Video

23 Apr 2017Pierre SermanetCorey LynchYevgen ChebotarJasmine HsuEric JangStefan SchaalSergey Levine

We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses. Imitation of human behavior requires a viewpoint-invariant representation that captures the relationships between end-effectors (hands or robot grippers) and the environment, object attributes, and body pose... (read more)

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