Related papers: A Picture is Worth a Thousand Words: Language Models Plan from Pixels

A Picture is Worth a Thousand Words: Language Models Plan from Pixels

URL: http://arxiv.org/abs/2303.09031v1
Date: Thu, 16 Mar 2023 02:02:18 GMT
Title: A Picture is Worth a Thousand Words: Language Models Plan from Pixels
Authors: Anthony Z. Liu, Lajanugen Logeswaran, Sungryull Sohn, Honglak Lee
Abstract summary: Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments. In this work, we explore the use of pre-trained language models (PLMs) to reason about plan sequences from text instructions in embodied visual environments.
Score: 53.85753597586226
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments. In this work, we explore the use of pre-trained language models (PLMs) to reason about plan sequences from text instructions in embodied visual environments. Prior PLM based approaches for planning either assume observations are available in the form of text (e.g., provided by a captioning model), reason about plans from the instruction alone, or incorporate information about the visual environment in limited ways (such as a pre-trained affordance function). In contrast, we show that PLMs can accurately plan even when observations are directly encoded as input prompts for the PLM. We show that this simple approach outperforms prior approaches in experiments on the ALFWorld and VirtualHome benchmarks.

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