Wenlong_huang Language Models as Zero Shot Planners Extracting Actionable Knowledge for Embodied Agents 2022

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1. Title: Language Models as Zero Shot Planners: Extracting Actionable Knowledge for Embodied Agents
2. Author: Wenlong Huang et. al.
3. Publish Year: Mar 2022
4. Review Date: Mon, Sep 19, 2022

Summary of paper

Motivation

• Large language models are learning general commonsense world knowledge.
• so this paper, the author investigate the possibility of grounding high-level tasks, expressed as natural language (e.g., “make breakfast”) to a chosen set of action steps (“open fridge”).

Contribution

• they found out that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into mid-level plans without any further training.
• they proposed several tools to improve executability of the model generation without invasive probing or modifications to the model.

Some key terms

What is prompt learning

Methodology

they will have 2 steps

1. in prompt learning way, convert the high level tasks into mid-level plans
2. convert the mid-level plans into admissible actions
3. loop

admissible action planning by semantic translation

• Many reasons cause the failure of mapping from free-form language to unambiguous actionable steps
  • the output does not follow pre-defined mapping of any atomic actions
    • e.g., “I first walk to the bedroom” is not of the format “walk to <PLACE>”
  • the output may refer to atomic action and objects using words unrecognisable by the environment
    • e.g., “microwave the chocolate milk” where “microwave” and “chocolate milk” cannot be mapped to precise action and objects.
  • the output contains lexically ambiguous words
    • e.g., open TV vs switch on TV
• SOLUTION: cosine similarity of the language embeddings of the action phrases …