🤗 LeRobot Notebooks#

← 教程资料库 · 环境与训练工具

本节介绍 LeRobot 的通用功能。请按目标机器人、数据集和运行环境配置示例；用于 AlohaMini 2 / 2 Pro 前，需要确认硬件与接口兼容性。

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.