Instructions to use Unskild/ACT-Pick with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use Unskild/ACT-Pick with LeRobot:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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Download README.md from Unskild/ACT-Pick: direct link, hf CLI and curl.
- Browser
- Download file 899 Bytes
-
https://huggingface.co/Unskild/ACT-Pick/resolve/main/README.md
- Command line
-
hf download hf://Unskild/ACT-Pick/README.md
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curl -L -o README.md https://huggingface.co/Unskild/ACT-Pick/resolve/main/README.md
899 Bytes
| library_name: pytorch | |
| tags: | |
| - robotics | |
| - manipulation | |
| - imitation-learning | |
| - act | |
| - lerobot | |
| # ACT-Pick | |
| ACT-Pick is an **Action Chunking Transformer (ACT)** policy trained using | |
| **LeRobot** for a tabletop **object pickup task**. | |
| ## Model Details | |
| - Policy Type: ACT | |
| - Learning Method: Imitation Learning | |
| - Framework: PyTorch | |
| ## Files | |
| - `model.safetensors` — trained policy weights | |
| - `config.json` — model configuration | |
| - `policy_preprocessor.json` — observation normalization config | |
| - `policy_preprocessor_step_3_normalizer_processor.safetensors` — observation normalizer | |
| - `policy_postprocessor.json` — action unnormalization config | |
| - `policy_postprocessor_step_0_unnormalizer_processor.safetensors` — action unnormalizer | |
| - `train_config.json` — training configuration | |
| ## Usage | |
| ```bash | |
| lerobot-eval \ | |
| --policy.repo_id=NEW_HF_USERNAME/ACT-Pick \ | |
| --policy.device=cuda | |
| ``` | |