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Planning is an important capability of artificial agents that performlong-horizon tasks in real-world environments. In this work, we explore the useof pre-trained language models (PLMs) to reason about plan sequences from textinstructions in embodied visual environments. Prior PLM based approaches forplanning 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 (suchas a pre-trained affordance function). In contrast, we show that PLMs canaccurately plan even when observations are directly encoded as input promptsfor the PLM. We show that this simple approach outperforms prior approaches inexperiments on the ALFWorld and VirtualHome benchmarks.

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