🤗 LeRobot Notebooks#

← Tutorial Library · Environment and Training Tools

This section introduces the general functions of LeRobot. Please configure the example according to the target robot, data set and operating environment; before using it with AlohaMini 2 / 2 Pro, you need to confirm the hardware and interface compatibility.

This repository contains example notebooks for using LeRobot. These notebooks demonstrate how to train policies on real or simulation datasets using standardized policies.

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

Open in Colab

Expected training time for 100k steps: ~1.5 hours on an NVIDIA A100 GPU with batch size of 64.

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

Open in Colab

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