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Official Code for "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents"
| Date | Stars |
|---|---|
| 2026-07-31 | 280 |
| 2026-08-02 | 280 |
| 2026-08-06 | 280 |
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[](https://colab.research.google.com/github/huangwl18/language-planner/blob/main/src/demo.ipynb)
## Language Models as Zero-Shot Planners:<br>Extracting Actionable Knowledge for Embodied Agents
#### [[Project Page]](https://huangwl18.github.io/language-planner/) [[Paper]](https://arxiv.org/pdf/2201.07207.pdf) [[Video]](https://www.youtube.com/watch?v=CkyugWI3_fc)
[Wenlong Huang](https://wenlong.page)<sup>1</sup>, [Pieter Abbeel](http://people.eecs.berkeley.edu/~pabbeel/)<sup>1</sup>, [Deepak Pathak](https://www.cs.cmu.edu/~dpathak/)\*<sup>2</sup>, [Igor Mordatch](https://scholar.google.com/citations?user=Vzr1RukAAAAJ&hl=en)\*<sup>3</sup> (*equal advising)
<sup>1</sup>University of California, Berkeley, <sup>2</sup>Carnegie Mellon University, <sup>3</sup>Google Brain<br/>
<img src="images/action-translation.gif" width="550">
This is the official demo code for our [Language Models as Zero-Shot Planners](https://huangwl18.github.io/language-planner/) paper. The code demonstrates how Large Language Models, such as GPT-3 and Codex, can generate action plans for complex human activities (e.g. "make breakfast"), even without any further training. The code can be used with any available language models from [OpenAI API](https://openai.com/api/) and [Huggingface Transformers](https://huggingface.co/docs/transformers/index) with a common interface.
If you find this work useful in your research, please cite using the following BibTeX:
```bibtex
@article{huang2022language,
title={Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents},
author={Huang, Wenlong and Abbeel, Pieter and Pathak, Deepak and Mordatch, Igor},
journal={arXiv preprint arXiv:2201.07207},
year={2022}
}
```
## Local Setup or [Open in Colab](https://colab.research.google.com/github/huangwl18/language-planner/blob/main/src/demo.ipynb)
### Requirements
- Python=3.6.13
- CUDA=11.3
### Setup Instructions
```Shell
git clone https://github.com/huangwl18/language-planner.git
cd language-planner/
conda create --name language-planner-env python=3.6.13
conda activate language-planner-env
pip install --upgrade pip
pip install -r requirements.txt
```
## Running Code
See [`demo.ipynb`](https://github.com/huangwl18/language-planner/blob/main/src/demo.ipynb) (or [](https://colab.research.google.com/github/huangwl18/language-planner/blob/main/src/demo.ipynb)) for a complete walk-through of our method. Feel free to experiment with any household tasks that you come up with (or any tasks beyond household domain if you provide necessary actions in [`available_actions.json`](https://github.com/huangwl18/language-planner/blob/main/src/available_actions.ipynb))!
**Note:**
- It is observed that best results can be obtained with larger language models. If you cannot run [Huggingface Transformers](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads) models locally or on Google Colab due to memory constraint, it is recommended to register an [OpenAI API](https://openai.com/api/) account and use GPT-3 or Codex (As of 01/2022, $18 free credits are awarded to new accounts and Codex series are free after [admitted from the waitlist](https://share.hsforms.com/1GzaACuXwSsmLKPfmphF_1w4sk30?)).
- Due to language models' high sensitivity to sampling hyperparameters, you may need to tune sampling hyperparameters for different models to obtain the best results.
- The code uses the list of available actions supported in [VirtualHome 1.0](https://github.com/xavierpuigf/virtualhome/tree/v1.0.0)'s [Evolving Graph Simulator](https://github.com/xavierpuigf/virtualhome/tree/v1.0.0/simulation). The available actions are stored in [`available_actions.json`](https://github.com/huangwl18/language-planner/blob/main/src/available_actions.json). The actions should support a largExcerpt of 4,704 characters
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matched fp:26d6756d6af3c946, topic:foundation-models, topic:language-model
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matched fp:26d6756d6af3c946, topic:embodied-ai