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* chore: update .gitignore * chore: update pre-commit * chore(deps): update pyproject * fix(ci): multiple fixes * chore: pre-commit apply * chore: address review comments * Update pyproject.toml Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com> Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> * chore(deps): add todo --------- Signed-off-by: Steven Palma <imstevenpmwork@ieee.org> Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com>
30 lines
2.9 KiB
Plaintext
30 lines
2.9 KiB
Plaintext
# 🤗 LeRobot Notebooks
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This repository contains example notebooks for using LeRobot. These notebooks demonstrate how to train policies on real or simulation datasets using standardized policies.
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---
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### Training ACT
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[ACT](https://huggingface.co/papers/2304.13705) (Action Chunking Transformer) is a transformer-based policy architecture for imitation learning that processes robot states and camera inputs to generate smooth, chunked action sequences.
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We provide a ready-to-run Google Colab notebook to help you train ACT policies using datasets from the Hugging Face Hub, with optional logging to Weights & Biases.
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| Notebook | Colab |
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| :------------------------------------------------------------------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [Train ACT with LeRobot](https://github.com/huggingface/notebooks/blob/main/lerobot/training-act.ipynb) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-act.ipynb) |
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Expected training time for 100k steps: ~1.5 hours on an NVIDIA A100 GPU with batch size of `64`.
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### Training SmolVLA
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[SmolVLA](https://huggingface.co/papers/2506.01844) is a small but efficient Vision-Language-Action model. It is compact in size with 450 M-parameter and is developed by Hugging Face.
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We provide a ready-to-run Google Colab notebook to help you train SmolVLA policies using datasets from the Hugging Face Hub, with optional logging to Weights & Biases.
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| Notebook | Colab |
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| :-------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| [Train SmolVLA with LeRobot](https://github.com/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb) |
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Expected training time for 20k steps: ~5 hours on an NVIDIA A100 GPU with batch size of `64`.
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