https://github.com/google-research/robotics_transformer

https://github.com/google-research/robotics_transformer

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/google-research/robotics_transformer
• Owner: google-research
• License: apache-2.0
• Created: 2022-12-05T00:39:23.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2024-01-31T15:45:35.000Z (10 months ago)
• Last Synced: 2024-10-01T10:42:59.114Z (about 1 month ago)
• Language: Python
• Size: 45.4 MB
• Stars: 1,319
• Watchers: 25
• Forks: 151
• Open Issues: 22
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# Robotics Transformer

*This is not an officially supported Google product.*

This repository is a collection code files and artifacts for running

Robotics Transformer or RT-1.

## Features

* Film efficient net based image tokenizer backbone

* Token learner based compression of input tokens

* Transformer for end to end robotic control

* Testing utilities

## Getting Started

### Installation

Clone the repo

```bash

git clone https://github.com/google-research/robotics_transformer.git

pip install -r robotics_transformer/requirements.txt

python -m robotics_transformer.tokenizers.action_tokenizer.test

```

### Running Tests

To run RT-1 tests, you can clone the git repo and run

[bazel](https://bazel.build/):

```bash

git clone https://github.com/google_research/robotics_transformer.git

cd robotics_transformer

bazel test ...

```

### Using trained checkpoints

Checkpoints are included in trained_checkpoints/ folder for three models:

1. [RT-1 trained on 700 tasks](trained_checkpoints/rt1main)

2. [RT-1 jointly trained on EDR and Kuka data](trained_checkpoints/rt1multirobot)

3. [RT-1 jointly trained on sim and real data](trained_checkpoints/rt1simreal)

They are tensorflow SavedModel files. Instructions on usage can be found [here](https://www.tensorflow.org/guide/saved_model)

## Future Releases

The current repository includes an initial set of libraries for early adoption.

More components may come in future releases.

## License

The Robotics Transformer library is licensed under the terms of the Apache

license.