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One-Shot Visual Imitation Learning via Meta-Learning
[article]
2017
arXiv
pre-print
In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural networks can enable a robot to represent complex skills, but learning each skill from scratch then becomes infeasible. In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new
arXiv:1709.04905v1
fatcat:vz5ykqgh3zg3nae6kzv77h5v2e