import{s as Rt,a as Ht,n as kt,o as Gt}from"../chunks/scheduler.eb244325.js";import{S as Nt,i as qt,e as a,s as l,c as s,h as zt,a as r,d as i,b as o,f as yt,g as d,j as g,k as B,l as z,m as n,n as p,t as c,o as m,p as u}from"../chunks/index.3fe63ad3.js";import{C as Pt}from"../chunks/CopyLLMTxtMenu.88bde5f2.js";import{C as Lt}from"../chunks/CodeBlock.934d290d.js";import{H as G,E as Zt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.341974a5.js";function Bt(Tt){let f,O,P,F,$,V,w,Y,y,_t='WALL-OSS is an open-source foundation model for embodied intelligence, proposed by the <a href="https://x2robot.com/en/research/68bc2cde8497d7f238dde690" rel="nofollow">XSquare Robot</a> team in 2025. The LeRobot implementation is adapted from their open-source <a href="https://github.com/X-Square-Robot/wall-x" rel="nofollow">WallX</a> repository.',Q,M,xt='X Square Robot’s WALL-OSS is now integrated into Hugging Face’s LeRobot ecosystem. This is an exciting collaborative project between the LeRobot and X Square Robot teams. You can now post-train, evaluate, and deploy WALL-OSS directly through LeRobot. With this, we’re aiming to make it easier for the open-source robotics community to customize and deploy WALL-OSS foundation models. Read and explore WALL-OSS <a href="https://arxiv.org/pdf/2509.11766" rel="nofollow">paper</a> and <a href="https://github.com/X-Square-Robot/wall-x" rel="nofollow">code</a>.',D,v,K,L,Ct="The WALL-OSS team is building the embodied foundation model to capture and compress the world’s most valuable data: the continuous, high-fidelity stream of physical interaction. By creating a direct feedback loop between the model’s decisions and the body’s lived experience, the emergence of a truly generalizable intelligence is enabled—one that understands not just how the world works, but how to act effectively within it.",tt,h,At,et,T,jt="Technically, WALL-OSS introduces a tightly coupled multimodal architecture (tightly-coupled MoE structure) that integrates both discrete and continuous action modeling strategies. Through a two-stage training pipeline (Inspiration → Integration), the model gradually unifies semantic reasoning and high-frequency action generation. Its core innovations include:",it,_,Ut=`<li><strong>Embodied perception–enhanced multimodal pretraining</strong>: Large-scale training on unified vision–language–action data to strengthen spatial, causal, and manipulation understanding.</li> <li><strong>Unified Cross-Level Chain-of-Thought (Uni-CoT)</strong>: A single differentiable framework that unifies high-level instruction reasoning, sub-task decomposition, and fine-grained action synthesis, forming a continuous chain from “understanding” to “execution.”</li> <li><strong>Mixture-of-Experts (MoE) action heads</strong>: Dynamically activating experts depending on the task phase and modeling actions in discrete or continuous space to maintain stable VLM priors.</li> <li><strong>Two-stage training paradigm</strong>:
<ul><li><strong>Inspiration stage</strong>: Injecting discrete action priors to strengthen spatial understanding and semantic-action alignment.</li> <li><strong>Integration stage</strong>: Using flow matching to achieve high-frequency continuous control.</li></ul></li>`,nt,x,lt,b,N,St='<p>Install LeRobot by following our <a href="./installation">Installation Guide</a>.</p>',Mt,C,q,Wt="Install WallX dependencies by running:",vt,A,ot,j,at,U,Xt="To use WallX in LeRobot, specify the policy type as:",rt,S,st,W,dt,X,Jt="For training WallX, you can use the standard LeRobot training script with the appropriate configuration:",pt,J,ct,E,mt,I,Et="<thead><tr><th>Argument</th> <th>Description</th></tr></thead> <tbody><tr><td><code>--dataset.repo_id</code></td> <td>The Hugging Face Hub repository ID for your training dataset (e.g., <code>lerobot/aloha_sim_insertion_human</code>)</td></tr> <tr><td><code>--policy.type</code></td> <td>Specifies using the WallX policy architecture</td></tr> <tr><td><code>--output_dir</code></td> <td>Local directory where training checkpoints and logs will be saved</td></tr> <tr><td><code>--job_name</code></td> <td>A name identifier for this training run (used in logging/tracking)</td></tr> <tr><td><code>--policy.repo_id</code></td> <td>Your Hugging Face Hub repo ID where the trained model will be pushed</td></tr> <tr><td><code>--policy.pretrained_path</code></td> <td>Path to pretrained WallX weights to initialize from (the official WALL-OSS checkpoint)</td></tr> <tr><td><code>--policy.prediction_mode</code></td> <td>The action prediction strategy: <code>diffusion</code> or <code>fast</code> - <code>diffusion</code> uses iterative denoising for action generation, <code>fast</code> uses next token prediction instead</td></tr> <tr><td><code>--policy.attn_implementation</code></td> <td>Attention implementation backend - <code>eager</code> uses standard PyTorch attention (alternatives include <code>flash_attention_2</code> or <code>sdpa</code>)</td></tr> <tr><td><code>--steps</code></td> <td>Total number of training steps to run</td></tr> <tr><td><code>--policy.device</code></td> <td>Device to train on (<code>cuda</code> for GPU, <code>cpu</code> for CPU)</td></tr> <tr><td><code>--batch_size</code></td> <td>Number of samples per training batch</td></tr></tbody>",ut,R,gt,H,It='This model follows the <strong>Apache 2.0 License</strong>, consistent with the original <a href="https://github.com/X-Square-Robot/wall-x" rel="nofollow">WallX repository</a>.',ft,k,ht,Z,bt;return $=new Pt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),w=new G({props:{title:"WALL-OSS",local:"wall-oss",headingTag:"h1"}}),v=new G({props:{title:"Model Overview",local:"model-overview",headingTag:"h2"}}),x=new G({props:{title:"Installation Requirements",local:"installation-requirements",headingTag:"h2"}}),A=new Lt({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1lJTIwJTIyLiU1QndhbGx4JTVEJTIy",highlighted:'pip install -e <span class="hljs-string">&quot;.[wallx]&quot;</span>',wrap:!1}}),j=new G({props:{title:"Usage",local:"usage",headingTag:"h2"}}),S=new Lt({props:{code:"cG9saWN5LnR5cGUlM0R3YWxsX3g=",highlighted:'policy.<span class="hljs-built_in">type</span>=wall_x',wrap:!1}}),W=new G({props:{title:"Training",local:"training",headingTag:"h2"}}),J=new Lt({props:{code:"bGVyb2JvdC10cmFpbiUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tZGF0YXNldC5yZXBvX2lkJTNEeW91cl9kYXRhc2V0JTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1wb2xpY3kudHlwZSUzRHdhbGxfeCUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tb3V0cHV0X2RpciUzRC4lMkZvdXRwdXRzJTJGd2FsbHhfdHJhaW5pbmclMjAlNUMlMEElMjAlMjAlMjAlMjAtLWpvYl9uYW1lJTNEd2FsbHhfdHJhaW5pbmclMjAlNUMlMEElMjAlMjAlMjAlMjAtLXBvbGljeS5yZXBvX2lkJTNEeW91cl9yZXBvX2lkJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1wb2xpY3kucHJldHJhaW5lZF9uYW1lX29yX3BhdGglM0R4LXNxdWFyZS1yb2JvdCUyRndhbGwtb3NzLWZsb3clMjAlNUMlMEElMjAlMjAlMjAlMjAtLXBvbGljeS5wcmVkaWN0aW9uX21vZGUlM0RkaWZmdXNpb24lMjAlNUMlMEElMjAlMjAlMjAlMjAtLXBvbGljeS5hdHRuX2ltcGxlbWVudGF0aW9uJTNEZWFnZXIlMjAlNUMlMEElMjAlMjAlMjAlMjAtLXN0ZXBzJTNEMzAwMCUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tcG9saWN5LmRldmljZSUzRGN1ZGElMjAlNUMlMEElMjAlMjAlMjAlMjAtLWJhdGNoX3NpemUlM0QzMg==",highlighted:`lerobot-train \\
    --dataset.repo_id=your_dataset \\
    --policy.type=wall_x \\
    --output_dir=./outputs/wallx_training \\
    --job_name=wallx_training \\
    --policy.repo_id=your_repo_id \\
    --policy.pretrained_name_or_path=x-square-robot/wall-oss-flow \\
    --policy.prediction_mode=diffusion \\
    --policy.attn_implementation=eager \\
    --steps=3000 \\
    --policy.device=cuda \\
    --batch_size=32`,wrap:!1}}),E=new G({props:{title:"Training Arguments",local:"training-arguments",headingTag:"h3"}}),R=new G({props:{title:"License",local:"license",headingTag:"h2"}}),k=new Zt({props:{source:"https://github.com/huggingface/lerobot/blob/main/docs/source/walloss.mdx"}}),{c(){f=a("meta"),O=l(),P=a("p"),F=l(),s($.$$.fragment),V=l(),s(w.$$.fragment),Y=l(),y=a("p"),y.innerHTML=_t,Q=l(),M=a("p"),M.innerHTML=xt,D=l(),s(v.$$.fragment),K=l(),L=a("p"),L.textContent=Ct,tt=l(),h=a("img"),et=l(),T=a("p"),T.textContent=jt,it=l(),_=a("ul"),_.innerHTML=Ut,nt=l(),s(x.$$.fragment),lt=l(),b=a("ol"),N=a("li"),N.innerHTML=St,Mt=l(),C=a("li"),q=a("p"),q.textContent=Wt,vt=l(),s(A.$$.fragment),ot=l(),s(j.$$.fragment),at=l(),U=a("p"),U.textContent=Xt,rt=l(),s(S.$$.fragment),st=l(),s(W.$$.fragment),dt=l(),X=a("p"),X.textContent=Jt,pt=l(),s(J.$$.fragment),ct=l(),s(E.$$.fragment),mt=l(),I=a("table"),I.innerHTML=Et,ut=l(),s(R.$$.fragment),gt=l(),H=a("p"),H.innerHTML=It,ft=l(),s(k.$$.fragment),ht=l(),Z=a("p"),this.h()},l(t){const e=zt("svelte-u9bgzb",document.head);f=r(e,"META",{name:!0,content:!0}),e.forEach(i),O=o(t),P=r(t,"P",{}),yt(P).forEach(i),F=o(t),d($.$$.fragment,t),V=o(t),d(w.$$.fragment,t),Y=o(t),y=r(t,"P",{"data-svelte-h":!0}),g(y)!=="svelte-8prbm6"&&(y.innerHTML=_t),Q=o(t),M=r(t,"P",{"data-svelte-h":!0}),g(M)!=="svelte-mngcq8"&&(M.innerHTML=xt),D=o(t),d(v.$$.fragment,t),K=o(t),L=r(t,"P",{"data-svelte-h":!0}),g(L)!=="svelte-1tjsmdq"&&(L.textContent=Ct),tt=o(t),h=r(t,"IMG",{src:!0,alt:!0,width:!0}),et=o(t),T=r(t,"P",{"data-svelte-h":!0}),g(T)!=="svelte-rdqih6"&&(T.textContent=jt),it=o(t),_=r(t,"UL",{"data-svelte-h":!0}),g(_)!=="svelte-5g51bj"&&(_.innerHTML=Ut),nt=o(t),d(x.$$.fragment,t),lt=o(t),b=r(t,"OL",{});var $t=yt(b);N=r($t,"LI",{"data-svelte-h":!0}),g(N)!=="svelte-19fosg9"&&(N.innerHTML=St),Mt=o($t),C=r($t,"LI",{});var wt=yt(C);q=r(wt,"P",{"data-svelte-h":!0}),g(q)!=="svelte-bua9t8"&&(q.textContent=Wt),vt=o(wt),d(A.$$.fragment,wt),wt.forEach(i),$t.forEach(i),ot=o(t),d(j.$$.fragment,t),at=o(t),U=r(t,"P",{"data-svelte-h":!0}),g(U)!=="svelte-1kwfsp0"&&(U.textContent=Xt),rt=o(t),d(S.$$.fragment,t),st=o(t),d(W.$$.fragment,t),dt=o(t),X=r(t,"P",{"data-svelte-h":!0}),g(X)!=="svelte-c9ntxz"&&(X.textContent=Jt),pt=o(t),d(J.$$.fragment,t),ct=o(t),d(E.$$.fragment,t),mt=o(t),I=r(t,"TABLE",{"data-svelte-h":!0}),g(I)!=="svelte-1bocqbj"&&(I.innerHTML=Et),ut=o(t),d(R.$$.fragment,t),gt=o(t),H=r(t,"P",{"data-svelte-h":!0}),g(H)!=="svelte-1hzggvi"&&(H.innerHTML=It),ft=o(t),d(k.$$.fragment,t),ht=o(t),Z=r(t,"P",{}),yt(Z).forEach(i),this.h()},h(){B(f,"name","hf:doc:metadata"),B(f,"content",Ot),Ht(h.src,At="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/walloss-lerobot-paper.png")||B(h,"src",At),B(h,"alt","An overview of WALL-OSS"),B(h,"width","85%")},m(t,e){z(document.head,f),n(t,O,e),n(t,P,e),n(t,F,e),p($,t,e),n(t,V,e),p(w,t,e),n(t,Y,e),n(t,y,e),n(t,Q,e),n(t,M,e),n(t,D,e),p(v,t,e),n(t,K,e),n(t,L,e),n(t,tt,e),n(t,h,e),n(t,et,e),n(t,T,e),n(t,it,e),n(t,_,e),n(t,nt,e),p(x,t,e),n(t,lt,e),n(t,b,e),z(b,N),z(b,Mt),z(b,C),z(C,q),z(C,vt),p(A,C,null),n(t,ot,e),p(j,t,e),n(t,at,e),n(t,U,e),n(t,rt,e),p(S,t,e),n(t,st,e),p(W,t,e),n(t,dt,e),n(t,X,e),n(t,pt,e),p(J,t,e),n(t,ct,e),p(E,t,e),n(t,mt,e),n(t,I,e),n(t,ut,e),p(R,t,e),n(t,gt,e),n(t,H,e),n(t,ft,e),p(k,t,e),n(t,ht,e),n(t,Z,e),bt=!0},p:kt,i(t){bt||(c($.$$.fragment,t),c(w.$$.fragment,t),c(v.$$.fragment,t),c(x.$$.fragment,t),c(A.$$.fragment,t),c(j.$$.fragment,t),c(S.$$.fragment,t),c(W.$$.fragment,t),c(J.$$.fragment,t),c(E.$$.fragment,t),c(R.$$.fragment,t),c(k.$$.fragment,t),bt=!0)},o(t){m($.$$.fragment,t),m(w.$$.fragment,t),m(v.$$.fragment,t),m(x.$$.fragment,t),m(A.$$.fragment,t),m(j.$$.fragment,t),m(S.$$.fragment,t),m(W.$$.fragment,t),m(J.$$.fragment,t),m(E.$$.fragment,t),m(R.$$.fragment,t),m(k.$$.fragment,t),bt=!1},d(t){t&&(i(O),i(P),i(F),i(V),i(Y),i(y),i(Q),i(M),i(D),i(K),i(L),i(tt),i(h),i(et),i(T),i(it),i(_),i(nt),i(lt),i(b),i(ot),i(at),i(U),i(rt),i(st),i(dt),i(X),i(pt),i(ct),i(mt),i(I),i(ut),i(gt),i(H),i(ft),i(ht),i(Z)),i(f),u($,t),u(w,t),u(v,t),u(x,t),u(A),u(j,t),u(S,t),u(W,t),u(J,t),u(E,t),u(R,t),u(k,t)}}}const Ot='{"title":"WALL-OSS","local":"wall-oss","sections":[{"title":"Model Overview","local":"model-overview","sections":[],"depth":2},{"title":"Installation Requirements","local":"installation-requirements","sections":[],"depth":2},{"title":"Usage","local":"usage","sections":[],"depth":2},{"title":"Training","local":"training","sections":[{"title":"Training Arguments","local":"training-arguments","sections":[],"depth":3}],"depth":2},{"title":"License","local":"license","sections":[],"depth":2}],"depth":1}';function Ft(Tt){return Gt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class te extends Nt{constructor(f){super(),qt(this,f,Ft,Bt,Rt,{})}}export{te as component};
