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        <title>Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions</title>
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        <namePart type="given">Peter</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>The recently proposed ALFRED challenge task aims for a virtual robotic agent to complete complex multi-step everyday tasks in a virtual home environment from high-level natural language directives, such as “put a hot piece of bread on a plate”. Currently, the best-performing models are able to complete less than 1% of these tasks successfully. In this work we focus on modeling the translation problem of converting natural language directives into detailed multi-step sequences of actions that accomplish those goals in the virtual environment. We empirically demonstrate that it is possible to generate gold multi-step plans from language directives alone without any visual input in 26% of unseen cases. When a small amount of visual information, the starting location in the virtual environment, is incorporated, our best-performing GPT-2 model successfully generates gold command sequences in 58% of cases, suggesting contextualized language models may provide strong planning modules for grounded virtual agents.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.395</identifier>
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%0 Conference Proceedings
%T Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions
%A Jansen, Peter
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F jansen-2020-visually
%X The recently proposed ALFRED challenge task aims for a virtual robotic agent to complete complex multi-step everyday tasks in a virtual home environment from high-level natural language directives, such as “put a hot piece of bread on a plate”. Currently, the best-performing models are able to complete less than 1% of these tasks successfully. In this work we focus on modeling the translation problem of converting natural language directives into detailed multi-step sequences of actions that accomplish those goals in the virtual environment. We empirically demonstrate that it is possible to generate gold multi-step plans from language directives alone without any visual input in 26% of unseen cases. When a small amount of visual information, the starting location in the virtual environment, is incorporated, our best-performing GPT-2 model successfully generates gold command sequences in 58% of cases, suggesting contextualized language models may provide strong planning modules for grounded virtual agents.
%R 10.18653/v1/2020.findings-emnlp.395
%U https://aclanthology.org/2020.findings-emnlp.395/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.395
%P 4412-4417

Markdown (Informal)

[Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions](https://aclanthology.org/2020.findings-emnlp.395/) (Jansen, Findings 2020)

• Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions (Jansen, Findings 2020)

ACL

• Peter Jansen. 2020. Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4412–4417, Online. Association for Computational Linguistics.