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		<link rel="modulepreload" href="/docs/robotics-course/pr_27/en/_app/immutable/chunks/MermaidChart.svelte_svelte_type_style_lang.4d36332b.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;LeRobot: An End-to-End Robot Learning Library&quot;,&quot;local&quot;:&quot;lerobot-an-end-to-end-robot-learning-library&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;References&quot;,&quot;local&quot;:&quot;references&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></button></div> </div>  <h1 class="relative group"><a id="lerobot-an-end-to-end-robot-learning-library" 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-an-end-to-end-robot-learning-library"><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: An End-to-End Robot Learning Library</span></h1> <img src="https://huggingface.co/robotics-course/images/resolve/main/ch1/ch1-lerobot-figure1.png" alt="LeRobot Library Overview" style="width: 100%;"> <p data-svelte-h="svelte-k657p6">Now that we’ve learned some history, let’s explore the main Python library we’ll be using throughout this course: LeRobot.</p> <p data-svelte-h="svelte-fnq8w0">LeRobot is an open-source library for robotics developed by Hugging Face. Think of it as a complete toolkit that handles everything from controlling real robots to training advanced learning algorithms, all in one place using PyTorch.</p> <p data-svelte-h="svelte-fwkufh">What makes LeRobot special is that it’s “vertically integrated.” This means it provides a unified way to work with real robots, handle complex multi-modal data (like combining camera feeds with sensor readings), and integrates smoothly with the PyTorch and Hugging Face tools you might already know. Essentially, LeRobot aims to be your one-stop library for robot learning projects.</p> <blockquote class="tip" data-svelte-h="svelte-euf418"><p><strong>Supported Robots:</strong> LeRobot currently supports accessible platforms such as <strong>SO-100/SO-101</strong> (3D‑printable arms) and <strong>ALOHA/ALOHA‑2</strong> (bimanual manipulation). For the up‑to‑date list of supported platforms, see the <a href="https://huggingface.co/docs/lerobot" rel="nofollow">official documentation</a>.</p></blockquote> <p data-svelte-h="svelte-op6kut">One key advantage is that LeRobot uses a standardized approach for connecting to different robot platforms. This means adding support for new robots requires much less work than starting from scratch. The library also introduces <code>LeRobotDataset</code>, a specialized format for robotics data that the open-source community is already using to share datasets efficiently.</p> <p data-svelte-h="svelte-sophub">LeRobot includes implementations of many cutting-edge robot learning algorithms, all built with PyTorch for efficiency. It also provides tools for running experiments and tracking results. Perhaps most importantly for real-world applications, LeRobot separates the “thinking” part (planning what to do) from the “doing” part (executing actions). This separation is crucial because it allows robots to react quickly and adapt better when things don’t go exactly as planned.</p> <blockquote class="warning" data-svelte-h="svelte-143oc4y"><p><strong>Performance Note:</strong> LeRobot’s optimized inference stack is crucial for real-time robot control, where delays of even milliseconds can affect performance. This separation of planning and execution is a key innovation.</p></blockquote>  <h2 class="relative group"><a id="references" 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="#references"><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>References</span></h2> <p data-svelte-h="svelte-12jfyok">For a full list of references, check out the <a href="https://huggingface.co/spaces/lerobot/robot-learning-tutorial" rel="nofollow">tutorial</a>.</p> <ul data-svelte-h="svelte-pxzxhw"><li><p><strong>RT-1: Robotics Transformer for Real-World Control at Scale</strong> (2023)<br>
Anthony Brohan et al.<br>
This paper demonstrates how transformer architectures can be applied to robotic control at scale, showing the power of learning from large and diverse datasets.<br> <a href="https://huggingface.co/papers/2212.06817" rel="nofollow">arXiv:2212.06817</a></p></li> <li><p><strong>Open X-Embodiment: Robotic Learning Datasets and RT-X Models</strong> (2023)<br>
Open X-Embodiment Collaboration<br>
A collaborative effort to create large-scale, diverse robotic datasets across multiple embodiments, demonstrating the importance of data sharing in advancing robot learning.<br> <a href="https://huggingface.co/papers/2310.08864" rel="nofollow">arXiv:2310.08864</a></p></li></ul> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/robotics-course/blob/main/units/en/unit1/2.mdx" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a>  <p></p> 
			
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