lerobot/pr_1713/en/notebooks.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;🤗 LeRobot Notebooks&quot;,&quot;local&quot;:&quot;-lerobot-notebooks&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Training ACT&quot;,&quot;local&quot;:&quot;training-act&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3},{&quot;title&quot;:&quot;Training SmolVLA&quot;,&quot;local&quot;:&quot;training-smolvla&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:1}">
		<link href="/docs/lerobot/pr_1713/en/_app/immutable/assets/0.e3b0c442.css" rel="modulepreload">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/entry/start.ff6c7d92.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/chunks/scheduler.f6b352c8.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/chunks/singletons.8fa76063.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/chunks/index.26cf6c5a.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/chunks/paths.e7fcefe1.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/entry/app.492c5e10.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/chunks/index.b90df637.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/nodes/0.5be955ad.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/chunks/each.e59479a4.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/nodes/17.f50d9560.js">
		<link rel="modulepreload" href="/docs/lerobot/pr_1713/en/_app/immutable/chunks/getInferenceSnippets.00196ff1.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;🤗 LeRobot Notebooks&quot;,&quot;local&quot;:&quot;-lerobot-notebooks&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Training ACT&quot;,&quot;local&quot;:&quot;training-act&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3},{&quot;title&quot;:&quot;Training SmolVLA&quot;,&quot;local&quot;:&quot;training-smolvla&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="-lerobot-notebooks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#-lerobot-notebooks"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>🤗 LeRobot Notebooks</span></h1> <p data-svelte-h="svelte-sl6xra">This repository contains example notebooks for using LeRobot. These notebooks demonstrate how to train policies on real or simulation datasets using standardized policies.</p> <hr>  <h3 class="relative group"><a id="training-act" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#training-act"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Training ACT</span></h3> <p data-svelte-h="svelte-1u2ckak"><a href="https://huggingface.co/papers/2304.13705" rel="nofollow">ACT</a> (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.</p> <p data-svelte-h="svelte-1fsfczu">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 &amp; Biases.</p> <table data-svelte-h="svelte-sjko0e"><thead><tr><th align="left">Notebook</th> <th align="left">Colab</th></tr></thead> <tbody><tr><td align="left"><a href="https://github.com/huggingface/notebooks/blob/main/lerobot/training-act.ipynb" rel="nofollow">Train ACT with LeRobot</a></td> <td align="left"><a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-act.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"></a></td></tr></tbody></table> <p data-svelte-h="svelte-b23k8a">Expected training time for 100k steps: ~1.5 hours on an NVIDIA A100 GPU with batch size of <code>64</code>.</p>  <h3 class="relative group"><a id="training-smolvla" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#training-smolvla"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Training SmolVLA</span></h3> <p data-svelte-h="svelte-iokth6"><a href="https://huggingface.co/papers/2506.01844" rel="nofollow">SmolVLA</a> is a small but efficient Vision-Language-Action model. It is compact in size with 450 M-parameter and is developed by Hugging Face.</p> <p data-svelte-h="svelte-jyr6s6">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 &amp; Biases.</p> <table data-svelte-h="svelte-1gsh8q6"><thead><tr><th align="left">Notebook</th> <th align="left">Colab</th></tr></thead> <tbody><tr><td align="left"><a href="https://github.com/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb" rel="nofollow">Train SmolVLA with LeRobot</a></td> <td align="left"><a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"></a></td></tr></tbody></table> <p data-svelte-h="svelte-1wxrw0s">Expected training time for 20k steps: ~5 hours on an NVIDIA A100 GPU with batch size of <code>64</code>.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/lerobot/blob/main/docs/source/notebooks.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1">&lt;</span> <span data-svelte-h="svelte-x0xyl0">&gt;</span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a>  <p></p> 
			
			<script>
				{
					__sveltekit_1l4d05w = {
						assets: "/docs/lerobot/pr_1713/en",
						base: "/docs/lerobot/pr_1713/en",
						env: {}
					};

					const element = document.currentScript.parentElement;

					const data = [null,null];

					Promise.all([
						import("/docs/lerobot/pr_1713/en/_app/immutable/entry/start.ff6c7d92.js"),
						import("/docs/lerobot/pr_1713/en/_app/immutable/entry/app.492c5e10.js")
					]).then(([kit, app]) => {
						kit.start(app, element, {
							node_ids: [0, 17],
							data,
							form: null,
							error: null
						});
					});
				}
			</script>