Skill Induction and Planning with Latent Language

• URL: http://arxiv.org/abs/2110.01517v1
• Date: Mon, 4 Oct 2021 15:36:32 GMT
• Title: Skill Induction and Planning with Latent Language
• Authors: Pratyusha Sharma, Antonio Torralba, Jacob Andreas
• Abstract summary: We formulate a generative model of action sequences in which goals generate sequences of high-level subtask descriptions. We describe how to train this model using primarily unannotated demonstrations by parsing demonstrations into sequences of named high-level subtasks. In trained models, the space of natural language commands indexes a library of skills; agents can use these skills to plan by generating high-level instruction sequences tailored to novel goals.
• Score: 94.55783888325165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 sequences of named high-level subtasks, using only a small number of seed annotations to ground language in action. In trained models, the space of natural language commands indexes a combinatorial library of skills; agents can use these skills to plan by generating high-level instruction sequences tailored to novel goals. We evaluate this approach in the ALFRED household simulation environment, providing natural language annotations for only 10% of demonstrations. It completes more than twice as many tasks as a standard approach to learning from demonstrations, matching the performance of instruction following models with access to ground-truth plans during both training and evaluation.

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