#
🤗 LeRobot Notebooks

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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Training ACT

ACT (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.

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.

Notebook | Colab
--- | ---
Train ACT with LeRobot |

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

SmolVLA is a small but efficient Vision-Language-Action model. It is compact in size with 450 M-parameter and is developed by Hugging Face.

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.

Notebook | Colab
--- | ---
Train SmolVLA with LeRobot |

Expected training time for 20k steps: ~5 hours on an NVIDIA A100 GPU with batch size of `64`.

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