import{s as Ee,n as Le,o as Qe}from"../chunks/scheduler.eb244325.js";import{S as Ye,i as Ne,e as i,s,c as o,h as xe,a as r,d as l,b as n,f as Ge,g as p,j as h,k as We,l as Ae,m as a,n as d,t as c,o as M,p as y}from"../chunks/index.3fe63ad3.js";import{C as Fe}from"../chunks/CopyLLMTxtMenu.88bde5f2.js";import{C as Ze}from"../chunks/CodeBlock.934d290d.js";import{H as w,E as He}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.341974a5.js";function _e(je){let m,F,x,H,T,_,J,R,u,Ue=`This repository contains the Hugging Face port of <strong>π₀.₅</strong>, adapted from <a href="https://github.com/Physical-Intelligence/openpi" rel="nofollow">OpenPI</a> by the Physical Intelligence.
It is designed as a <strong>Vision-Language-Action model with open-world generalization</strong>.`,S,V,X,j,z,U,be="<thead><tr><th>Feature</th> <th>π₀</th> <th>π₀.₅</th></tr></thead> <tbody><tr><td>Time Conditioning</td> <td>Concatenates time with actions via <code>action_time_mlp_*</code></td> <td>Uses <code>time_mlp_*</code> for AdaRMS conditioning</td></tr> <tr><td>AdaRMS</td> <td>Not used</td> <td>Used in action expert</td></tr> <tr><td>Tokenizer Length</td> <td>48 tokens</td> <td>200 tokens</td></tr> <tr><td>Discrete State Input</td> <td>False (Uses <code>state_proj</code> layer)</td> <td>True</td></tr> <tr><td>Parameter Count</td> <td>Higher (includes state embedding)</td> <td>Lower (no state embedding)</td></tr></tbody>",P,D,O,b,q,I,Ie=`π₀.₅ supports training with <strong>relative actions</strong>, where the model learns relative offsets
from the current robot state instead of absolute joint positions. This mirrors the
relative-action transform in OpenPI (<code>DeltaActions</code>) and can improve performance.`,K,f,ee,g,fe=`<li><strong>During preprocessing</strong>, absolute actions are converted to relative offsets:
<code>relative = action - state</code> (for selected joints).</li> <li>The relative actions are normalized using statistics computed from the relative distribution.</li> <li><strong>During postprocessing</strong>, predicted relative actions are converted back to absolute:
<code>absolute = relative + state</code>.</li>`,te,C,ge="Joints listed in <code>relative_exclude_joints</code> (e.g., gripper) are kept absolute.",le,$,ae,k,Ce="<thead><tr><th>Parameter</th> <th>Type</th> <th>Default</th> <th>Description</th></tr></thead> <tbody><tr><td><code>use_relative_actions</code></td> <td><code>bool</code></td> <td><code>False</code></td> <td>Enable relative-action training</td></tr> <tr><td><code>relative_exclude_joints</code></td> <td><code>list[str]</code></td> <td><code>[&quot;gripper&quot;]</code></td> <td>Joint names to keep absolute (matched by substring)</td></tr> <tr><td><code>action_feature_names</code></td> <td><code>list[str]</code></td> <td><code>None</code></td> <td>Auto-populated from dataset metadata at runtime by <code>make_policy</code></td></tr></tbody>",se,B,ne,v,ie,G,$e="When <code>use_relative_actions=true</code>, the training script automatically:",re,W,ke="<li>Computes relative action statistics from the dataset (sampled chunk-level relative actions)</li> <li>Replaces the standard action stats with relative stats for normalization</li> <li>Broadcasts these stats across all ranks in distributed training</li>",oe,pe,de,Z,ce,E,Be="If you use this work, please cite both <strong>OpenPI</strong> and the π₀.₅ paper:",Me,L,ye,he,me,Q,we,Y,ve='This port follows the <strong>Apache 2.0 License</strong>, consistent with the original <a href="https://github.com/Physical-Intelligence/openpi" rel="nofollow">OpenPI repository</a>.',Te,N,Je,A,ue;return T=new Fe({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),J=new w({props:{title:"π₀.₅ (pi05)",local:"π₀₅-pi05",headingTag:"h1"}}),j=new w({props:{title:"Model Overview",local:"model-overview",headingTag:"h2"}}),b=new w({props:{title:"Relative Actions",local:"relative-actions",headingTag:"h2"}}),f=new w({props:{title:"How it works",local:"how-it-works",headingTag:"h3"}}),$=new w({props:{title:"Configuration",local:"configuration",headingTag:"h3"}}),B=new w({props:{title:"Training example",local:"training-example",headingTag:"h3"}}),v=new Ze({props:{code:"cHl0aG9uJTIwLW0lMjBsZXJvYm90LnNjcmlwdHMubGVyb2JvdF90cmFpbiUyMCU1QyUwQSUyMCUyMC0tcG9saWN5LnR5cGUlM0RwaTA1JTIwJTVDJTBBJTIwJTIwLS1kYXRhc2V0LnJlcG9faWQlM0R5b3VyX29yZyUyRnlvdXJfZGF0YXNldCUyMCU1QyUwQSUyMCUyMC0tcG9saWN5LnVzZV9yZWxhdGl2ZV9hY3Rpb25zJTNEdHJ1ZSUyMCU1QyUwQSUyMCUyMC0tcG9saWN5LnJlbGF0aXZlX2V4Y2x1ZGVfam9pbnRzJTNEJyU1QiUyMmdyaXBwZXIlMjIlNUQn",highlighted:`python -m lerobot.scripts.lerobot_train \\
  --policy.type=pi05 \\
  --dataset.repo_id=your_org/your_dataset \\
  --policy.use_relative_actions=<span class="hljs-literal">true</span> \\
  --policy.relative_exclude_joints=<span class="hljs-string">&#x27;[&quot;gripper&quot;]&#x27;</span>`,wrap:!1}}),Z=new w({props:{title:"Citation",local:"citation",headingTag:"h2"}}),L=new Ze({props:{code:"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",highlighted:`<span class="hljs-comment">@misc{openpi2024,</span>
  author       = {Physical Intelligence Lab},
  title        = {OpenPI: PyTorch Implementation of π<span class="hljs-number">0</span> <span class="hljs-keyword">and</span> π<span class="hljs-number">0</span>.<span class="hljs-number">5</span> Policies},
  year         = {<span class="hljs-number">2024</span>},
  publisher    = {GitHub},
  howpublished = {\\url{https:<span class="hljs-comment">//github.com/Physical-Intelligence/openpi}},</span>
  license      = {Apache-<span class="hljs-number">2</span>.<span class="hljs-number">0</span>}
}

<span class="hljs-comment">@misc{intelligence2025pi05visionlanguageactionmodelopenworld,</span>
  title        = {π₀.₅: a Vision-Language-Action Model with Open-World Generalization},
  author       = {Physical Intelligence <span class="hljs-keyword">and</span> Kevin Black <span class="hljs-keyword">and</span> Noah Brown <span class="hljs-keyword">and</span> James Darpinian <span class="hljs-keyword">and</span> Karan Dhabalia <span class="hljs-keyword">and</span> Danny Driess <span class="hljs-keyword">and</span> Adnan Esmail <span class="hljs-keyword">and</span> Michael Equi <span class="hljs-keyword">and</span> Chelsea Finn <span class="hljs-keyword">and</span> Niccolo Fusai <span class="hljs-keyword">and</span> Manuel Y. Galliker <span class="hljs-keyword">and</span> Dibya Ghosh <span class="hljs-keyword">and</span> Lachy Groom <span class="hljs-keyword">and</span> Karol Hausman <span class="hljs-keyword">and</span> Brian Ichter <span class="hljs-keyword">and</span> Szymon Jakubczak <span class="hljs-keyword">and</span> Tim Jones <span class="hljs-keyword">and</span> Liyiming Ke <span class="hljs-keyword">and</span> Devin LeBlanc <span class="hljs-keyword">and</span> Sergey Levine <span class="hljs-keyword">and</span> Adrian Li-Bell <span class="hljs-keyword">and</span> Mohith Mothukuri <span class="hljs-keyword">and</span> Suraj Nair <span class="hljs-keyword">and</span> Karl Pertsch <span class="hljs-keyword">and</span> Allen Z. Ren <span class="hljs-keyword">and</span> Lucy Xiaoyang Shi <span class="hljs-keyword">and</span> Laura Smith <span class="hljs-keyword">and</span> Jost Tobias Springenberg <span class="hljs-keyword">and</span> Kyle Stachowicz <span class="hljs-keyword">and</span> James Tanner <span class="hljs-keyword">and</span> Quan Vuong <span class="hljs-keyword">and</span> Homer Walke <span class="hljs-keyword">and</span> Anna Walling <span class="hljs-keyword">and</span> Haohuan Wang <span class="hljs-keyword">and</span> Lili Yu <span class="hljs-keyword">and</span> Ury Zhilinsky},
  year         = {<span class="hljs-number">2025</span>},
  eprint       = {<span class="hljs-number">2504</span>.<span class="hljs-number">16054</span>},
  archivePrefix= {arXiv},
  primaryClass = {cs.LG},
  url          = {https:<span class="hljs-comment">//arxiv.org/abs/2504.16054},</span>
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