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Skill Induction and Planning with Latent Language
[article]
2022
arXiv
pre-print
We present a framework for learning hierarchical policies from demonstrations, using sparse natural language annotations to guide the discovery of reusable skills for autonomous decision-making. We formulate a generative model of action sequences in which goals generate sequences of high-level subtask descriptions, and these descriptions generate sequences of low-level actions. We describe how to train this model using primarily unannotated demonstrations by parsing demonstrations into
arXiv:2110.01517v2
fatcat:rw32yof5e5achfpcisrrn3op34