Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents (+Author)

#gpt3 #embodied #planning

In this video: Paper explanation, followed by first author interview with Wenlong Huang.

Large language models contain extraordinary amounts of world knowledge that can be queried in various ways. But their output format is largely uncontrollable. This paper investigates the VirtualHome environment, which expects a particular set of actions, objects, and verbs to be used. Turns out, with proper techniques and only using pre-trained models (no fine-tuning), one can translate unstructured language model outputs into the structured grammar of the environment. This is potentially very useful anywhere where the models' world knowledge needs to be provided in a particular structured format.

OUTLINE:

0:00 - Intro & Overview

2:45 - The VirtualHome environment

6:25 - The problem of plan evaluation

8:40 - Contributions of this paper

16:40 - Start of interview

24:00 - How to use language models with environments?

34:00 - What does model size matter?

40:00 - How to fix the large models' outputs?

55:00 - Possible improvements to the translation procedure

59:00 - Why does Codex perform so well?

1:02:15 - Diving into experimental results

1:14:15 - Future outlook

Paper: https://arxiv.org/abs/2201.07207

Website: https://wenlong.page/language-planner/

Code: https://github.com/huangwl18/language...

Wenlong's Twitter: https://twitter.com/wenlong_huang

Abstract:

Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks, expressed in natural language (e.g. "make breakfast"), to a chosen set of actionable steps (e.g. "open fridge"). While prior work focused on learning from explicit step-by-step examples of how to act, we surprisingly find that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into low-level plans without any further training. However, the plans produced naively by LLMs often cannot map precisely to admissible actions. We propose a procedure that conditions on existing demonstrations and semantically translates the plans to admissible actions. Our evaluation in the recent VirtualHome environment shows that the resulting method substantially improves executability over the LLM baseline. The conducted human evaluation reveals a trade-off between executability and correctness but shows a promising sign towards extracting actionable knowledge from language models. Website at this https URL

Authors: Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch

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