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- W4287659311 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 5% 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 is incorporated, namely the starting location in the virtual environment, our best-performing GPT-2 model successfully generates gold command sequences in 58% of cases. Our results suggest that contextualized language models may provide strong visual semantic planning modules for grounded virtual agents." @default.
- W4287659311 created "2022-07-25" @default.
- W4287659311 creator A5007471680 @default.
- W4287659311 date "2020-09-29" @default.
- W4287659311 modified "2023-09-28" @default.
- W4287659311 title "Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions" @default.
- W4287659311 doi "https://doi.org/10.48550/arxiv.2009.14259" @default.
- W4287659311 hasPublicationYear "2020" @default.
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