import{s as Jt,a as _t,n as xt,o as It}from"../chunks/scheduler.eb244325.js";import{S as Lt,i as jt,e as s,s as n,c as o,h as Rt,a as r,d as i,b as a,f as tt,g as p,j as m,k as me,l as se,m as l,n as c,t as d,o as f,p as u}from"../chunks/index.3fe63ad3.js";import{C as Vt}from"../chunks/CopyLLMTxtMenu.88bde5f2.js";import{C as re}from"../chunks/CodeBlock.934d290d.js";import{H as y,E as Xt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.341974a5.js";function Zt(nt){let g,ce,oe,de,$,fe,w,ue,b,at='π₀ is a <strong>Vision-Language-Action model for general robot control</strong>, from Physical Intelligence. The LeRobot implementation is adapted from their open source <a href="https://github.com/Physical-Intelligence/openpi" rel="nofollow">OpenPI</a> repository.',ye,T,ge,U,st='π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by <a href="https://www.physicalintelligence.company/blog/pi0" rel="nofollow">Physical Intelligence</a>. Unlike traditional robot programs that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks.',he,h,rt,Me,C,$e,v,ot="As described by Physical Intelligence, while AI has achieved remarkable success in digital domains, from chess-playing to drug discovery, human intelligence still dramatically outpaces AI in the physical world. To paraphrase Moravec’s paradox, winning a game of chess represents an “easy” problem for AI, but folding a shirt or cleaning up a table requires solving some of the most difficult engineering problems ever conceived. π₀ represents a first step toward developing artificial physical intelligence that enables users to simply ask robots to perform any task they want, just like they can with large language models.",we,J,be,_,pt="π₀ combines several key innovations:",Te,x,mt="<li><strong>Flow Matching</strong>: Uses a novel method to augment pre-trained VLMs with continuous action outputs via flow matching (a variant of diffusion models)</li> <li><strong>Cross-Embodiment Training</strong>: Trained on data from 8 distinct robot platforms including UR5e, Bimanual UR5e, Franka, Bimanual Trossen, Bimanual ARX, Mobile Trossen, and Mobile Fibocom</li> <li><strong>Internet-Scale Pre-training</strong>: Inherits semantic knowledge from a pre-trained 3B parameter Vision-Language Model</li> <li><strong>High-Frequency Control</strong>: Outputs motor commands at up to 50 Hz for real-time dexterous manipulation</li>",Ue,I,Ce,M,ne,ct='<p>Install LeRobot by following our <a href="./installation">Installation Guide</a>.</p>',it,L,ae,dt="Install Pi0 dependencies by running:",lt,j,ve,R,Je,V,ft="π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:",_e,X,ut="<li><strong>Internet-Scale Pre-training</strong>: Vision-language data from the web for semantic understanding</li> <li><strong>Open X-Embodiment Dataset</strong>: Open-source robot manipulation datasets</li> <li><strong>Physical Intelligence Dataset</strong>: Large and diverse dataset of dexterous tasks across 8 distinct robots</li>",xe,Z,Ie,G,yt="To use π₀ in LeRobot, specify the policy type as:",Le,k,je,Q,Re,B,gt="For training π₀, you can use the standard LeRobot training script with the appropriate configuration:",Ve,H,Xe,z,Ze,P,ht=`<li><strong><code>--policy.compile_model=true</code></strong>: Enables model compilation for faster training</li> <li><strong><code>--policy.gradient_checkpointing=true</code></strong>: Reduces memory usage significantly during training</li> <li><strong><code>--policy.dtype=bfloat16</code></strong>: Use mixed precision training for efficiency</li> <li><strong><code>--batch_size=32</code></strong>: Batch size for training, adapt this based on your GPU memory</li> <li><strong><code>--policy.pretrained_path=lerobot/pi0_base</code></strong>: The base π₀ model you want to finetune, options are:
<ul><li><a href="https://huggingface.co/lerobot/pi0_base" rel="nofollow">lerobot/pi0_base</a></li> <li><a href="https://huggingface.co/lerobot/pi0_libero" rel="nofollow">lerobot/pi0_libero</a> (specifically trained on the Libero dataset)</li></ul></li>`,Ge,S,ke,W,Mt="<thead><tr><th>Parameter</th> <th>Default</th> <th>Description</th></tr></thead> <tbody><tr><td><code>freeze_vision_encoder</code></td> <td><code>false</code></td> <td>Do not freeze the vision encoder</td></tr> <tr><td><code>train_expert_only</code></td> <td><code>false</code></td> <td>Do not freeze the VLM, train all parameters</td></tr></tbody>",Qe,Y,$t="<strong>💡 Tip</strong>: Setting <code>train_expert_only=true</code> freezes the VLM and trains only the action expert and projections, allowing finetuning with reduced memory usage.",Be,A,He,N,wt="By default, π₀ predicts absolute actions. You can enable <strong>relative actions</strong> so the model predicts offsets relative to the current robot state. This can improve training stability for certain setups.",ze,F,bt="To use relative actions, first recompute your dataset stats in relative space via the CLI:",Pe,E,Se,D,Tt="Or equivalently in Python:",We,q,Ye,O,Ut="The <code>chunk_size</code> should match your policy’s <code>chunk_size</code> (default 50 for π₀). <code>relative_exclude_joints</code> lists joint names that should remain in absolute space (e.g. gripper commands). Use <code>--push_to_hub true</code> to upload the updated stats to the Hub.",Ae,K,Ct="Then train with relative actions enabled:",Ne,ee,Fe,te,Ee,ie,vt='This model 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>.',De,le,qe,pe,Oe;return $=new Vt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),w=new y({props:{title:"π₀ (Pi0)",local:"π₀-pi0",headingTag:"h1"}}),T=new y({props:{title:"Model Overview",local:"model-overview",headingTag:"h2"}}),C=new y({props:{title:"The Vision for Physical Intelligence",local:"the-vision-for-physical-intelligence",headingTag:"h3"}}),J=new y({props:{title:"Architecture and Approach",local:"architecture-and-approach",headingTag:"h3"}}),I=new y({props:{title:"Installation Requirements",local:"installation-requirements",headingTag:"h2"}}),j=new re({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1lJTIwJTIyLiU1QnBpJTVEJTIy",highlighted:'pip install -e <span class="hljs-string">&quot;.[pi]&quot;</span>',wrap:!1}}),R=new y({props:{title:"Training Data and Capabilities",local:"training-data-and-capabilities",headingTag:"h2"}}),Z=new y({props:{title:"Usage",local:"usage",headingTag:"h2"}}),k=new re({props:{code:"cG9saWN5LnR5cGUlM0RwaTA=",highlighted:'policy.<span class="hljs-built_in">type</span>=pi0',wrap:!1}}),Q=new y({props:{title:"Training",local:"training",headingTag:"h2"}}),H=new re({props:{code:"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",highlighted:`lerobot-train \\
    --dataset.repo_id=your_dataset \\
    --policy.type=pi0 \\
    --output_dir=./outputs/pi0_training \\
    --job_name=pi0_training \\
    --policy.pretrained_path=lerobot/pi0_base \\
    --policy.repo_id=your_repo_id \\
    --policy.compile_model=<span class="hljs-literal">true</span> \\
    --policy.gradient_checkpointing=<span class="hljs-literal">true</span> \\
    --policy.dtype=bfloat16 \\
    --policy.freeze_vision_encoder=<span class="hljs-literal">false</span> \\
    --policy.train_expert_only=<span class="hljs-literal">false</span> \\
    --steps=3000 \\
    --policy.device=cuda \\
    --batch_size=32`,wrap:!1}}),z=new y({props:{title:"Key Training Parameters",local:"key-training-parameters",headingTag:"h3"}}),S=new y({props:{title:"Training Parameters Explained",local:"training-parameters-explained",headingTag:"h3"}}),A=new y({props:{title:"Relative Actions",local:"relative-actions",headingTag:"h2"}}),E=new re({props:{code:"bGVyb2JvdC1lZGl0LWRhdGFzZXQlMjAlNUMlMEElMjAlMjAlMjAlMjAtLXJlcG9faWQlMjB5b3VyX2RhdGFzZXQlMjAlNUMlMEElMjAlMjAlMjAlMjAtLW9wZXJhdGlvbi50eXBlJTIwcmVjb21wdXRlX3N0YXRzJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1vcGVyYXRpb24ucmVsYXRpdmVfYWN0aW9uJTIwdHJ1ZSUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tb3BlcmF0aW9uLmNodW5rX3NpemUlMjA1MCUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tb3BlcmF0aW9uLnJlbGF0aXZlX2V4Y2x1ZGVfam9pbnRzJTIwJTIyJTVCJ2dyaXBwZXInJTVEJTIyJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1wdXNoX3RvX2h1YiUyMHRydWU=",highlighted:`lerobot-edit-dataset \\
    --repo_id your_dataset \\
    --operation.type recompute_stats \\
    --operation.relative_action <span class="hljs-literal">true</span> \\
    --operation.chunk_size 50 \\
    --operation.relative_exclude_joints <span class="hljs-string">&quot;[&#x27;gripper&#x27;]&quot;</span> \\
    --push_to_hub <span class="hljs-literal">true</span>`,wrap:!1}}),q=new re({props:{code:"ZnJvbSUyMGxlcm9ib3QuZGF0YXNldHMlMjBpbXBvcnQlMjBMZVJvYm90RGF0YXNldCUyQyUyMHJlY29tcHV0ZV9zdGF0cyUwQSUwQWRhdGFzZXQlMjAlM0QlMjBMZVJvYm90RGF0YXNldCglMjJ5b3VyX2RhdGFzZXQlMjIpJTBBcmVjb21wdXRlX3N0YXRzKGRhdGFzZXQlMkMlMjByZWxhdGl2ZV9hY3Rpb24lM0RUcnVlJTJDJTIwY2h1bmtfc2l6ZSUzRDUwJTJDJTIwcmVsYXRpdmVfZXhjbHVkZV9qb2ludHMlM0QlNUIlMjJncmlwcGVyJTIyJTVEKSUwQWRhdGFzZXQucHVzaF90b19odWIoKQ==",highlighted:`<span class="hljs-keyword">from</span> lerobot.datasets <span class="hljs-keyword">import</span> LeRobotDataset, recompute_stats

dataset = LeRobotDataset(<span class="hljs-string">&quot;your_dataset&quot;</span>)
recompute_stats(dataset, relative_action=<span class="hljs-literal">True</span>, chunk_size=<span class="hljs-number">50</span>, relative_exclude_joints=[<span class="hljs-string">&quot;gripper&quot;</span>])
dataset.push_to_hub()`,wrap:!1}}),ee=new re({props:{code:"bGVyb2JvdC10cmFpbiUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tZGF0YXNldC5yZXBvX2lkJTNEeW91cl9kYXRhc2V0JTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1wb2xpY3kudHlwZSUzRHBpMCUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tcG9saWN5LnVzZV9yZWxhdGl2ZV9hY3Rpb25zJTNEdHJ1ZSUyMCU1QyUwQSUyMCUyMCUyMCUyMC0tcG9saWN5LnJlbGF0aXZlX2V4Y2x1ZGVfam9pbnRzJTNEJyU1QiUyMmdyaXBwZXIlMjIlNUQnJTIwJTVDJTBBJTIwJTIwJTIwJTIwLi4u",highlighted:`lerobot-train \\
    --dataset.repo_id=your_dataset \\
    --policy.type=pi0 \\
    --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}}),te=new y({props:{title:"License",local:"license",headingTag:"h2"}}),le=new Xt({props:{source:"https://github.com/huggingface/lerobot/blob/main/docs/source/pi0.mdx"}}),{c(){g=s("meta"),ce=n(),oe=s("p"),de=n(),o($.$$.fragment),fe=n(),o(w.$$.fragment),ue=n(),b=s("p"),b.innerHTML=at,ye=n(),o(T.$$.fragment),ge=n(),U=s("p"),U.innerHTML=st,he=n(),h=s("img"),Me=n(),o(C.$$.fragment),$e=n(),v=s("p"),v.textContent=ot,we=n(),o(J.$$.fragment),be=n(),_=s("p"),_.textContent=pt,Te=n(),x=s("ul"),x.innerHTML=mt,Ue=n(),o(I.$$.fragment),Ce=n(),M=s("ol"),ne=s("li"),ne.innerHTML=ct,it=n(),L=s("li"),ae=s("p"),ae.textContent=dt,lt=n(),o(j.$$.fragment),ve=n(),o(R.$$.fragment),Je=n(),V=s("p"),V.textContent=ft,_e=n(),X=s("ol"),X.innerHTML=ut,xe=n(),o(Z.$$.fragment),Ie=n(),G=s("p"),G.textContent=yt,Le=n(),o(k.$$.fragment),je=n(),o(Q.$$.fragment),Re=n(),B=s("p"),B.textContent=gt,Ve=n(),o(H.$$.fragment),Xe=n(),o(z.$$.fragment),Ze=n(),P=s("ul"),P.innerHTML=ht,Ge=n(),o(S.$$.fragment),ke=n(),W=s("table"),W.innerHTML=Mt,Qe=n(),Y=s("p"),Y.innerHTML=$t,Be=n(),o(A.$$.fragment),He=n(),N=s("p"),N.innerHTML=wt,ze=n(),F=s("p"),F.textContent=bt,Pe=n(),o(E.$$.fragment),Se=n(),D=s("p"),D.textContent=Tt,We=n(),o(q.$$.fragment),Ye=n(),O=s("p"),O.innerHTML=Ut,Ae=n(),K=s("p"),K.textContent=Ct,Ne=n(),o(ee.$$.fragment),Fe=n(),o(te.$$.fragment),Ee=n(),ie=s("p"),ie.innerHTML=vt,De=n(),o(le.$$.fragment),qe=n(),pe=s("p"),this.h()},l(e){const t=Rt("svelte-u9bgzb",document.head);g=r(t,"META",{name:!0,content:!0}),t.forEach(i),ce=a(e),oe=r(e,"P",{}),tt(oe).forEach(i),de=a(e),p($.$$.fragment,e),fe=a(e),p(w.$$.fragment,e),ue=a(e),b=r(e,"P",{"data-svelte-h":!0}),m(b)!=="svelte-99h03s"&&(b.innerHTML=at),ye=a(e),p(T.$$.fragment,e),ge=a(e),U=r(e,"P",{"data-svelte-h":!0}),m(U)!=="svelte-k3gmsj"&&(U.innerHTML=st),he=a(e),h=r(e,"IMG",{src:!0,alt:!0,width:!0}),Me=a(e),p(C.$$.fragment,e),$e=a(e),v=r(e,"P",{"data-svelte-h":!0}),m(v)!=="svelte-1db52yk"&&(v.textContent=ot),we=a(e),p(J.$$.fragment,e),be=a(e),_=r(e,"P",{"data-svelte-h":!0}),m(_)!=="svelte-ovjk2v"&&(_.textContent=pt),Te=a(e),x=r(e,"UL",{"data-svelte-h":!0}),m(x)!=="svelte-jiesd4"&&(x.innerHTML=mt),Ue=a(e),p(I.$$.fragment,e),Ce=a(e),M=r(e,"OL",{});var Ke=tt(M);ne=r(Ke,"LI",{"data-svelte-h":!0}),m(ne)!=="svelte-19fosg9"&&(ne.innerHTML=ct),it=a(Ke),L=r(Ke,"LI",{});var et=tt(L);ae=r(et,"P",{"data-svelte-h":!0}),m(ae)!=="svelte-xfhj0t"&&(ae.textContent=dt),lt=a(et),p(j.$$.fragment,et),et.forEach(i),Ke.forEach(i),ve=a(e),p(R.$$.fragment,e),Je=a(e),V=r(e,"P",{"data-svelte-h":!0}),m(V)!=="svelte-ryh1sd"&&(V.textContent=ft),_e=a(e),X=r(e,"OL",{"data-svelte-h":!0}),m(X)!=="svelte-1i19fvs"&&(X.innerHTML=ut),xe=a(e),p(Z.$$.fragment,e),Ie=a(e),G=r(e,"P",{"data-svelte-h":!0}),m(G)!=="svelte-4bbizg"&&(G.textContent=yt),Le=a(e),p(k.$$.fragment,e),je=a(e),p(Q.$$.fragment,e),Re=a(e),B=r(e,"P",{"data-svelte-h":!0}),m(B)!=="svelte-1ja7unj"&&(B.textContent=gt),Ve=a(e),p(H.$$.fragment,e),Xe=a(e),p(z.$$.fragment,e),Ze=a(e),P=r(e,"UL",{"data-svelte-h":!0}),m(P)!=="svelte-1sirz4"&&(P.innerHTML=ht),Ge=a(e),p(S.$$.fragment,e),ke=a(e),W=r(e,"TABLE",{"data-svelte-h":!0}),m(W)!=="svelte-4to8zr"&&(W.innerHTML=Mt),Qe=a(e),Y=r(e,"P",{"data-svelte-h":!0}),m(Y)!=="svelte-1ftxfxe"&&(Y.innerHTML=$t),Be=a(e),p(A.$$.fragment,e),He=a(e),N=r(e,"P",{"data-svelte-h":!0}),m(N)!=="svelte-11vbq72"&&(N.innerHTML=wt),ze=a(e),F=r(e,"P",{"data-svelte-h":!0}),m(F)!=="svelte-23lm5x"&&(F.textContent=bt),Pe=a(e),p(E.$$.fragment,e),Se=a(e),D=r(e,"P",{"data-svelte-h":!0}),m(D)!=="svelte-6r9mih"&&(D.textContent=Tt),We=a(e),p(q.$$.fragment,e),Ye=a(e),O=r(e,"P",{"data-svelte-h":!0}),m(O)!=="svelte-13yzagx"&&(O.innerHTML=Ut),Ae=a(e),K=r(e,"P",{"data-svelte-h":!0}),m(K)!=="svelte-k3mxj"&&(K.textContent=Ct),Ne=a(e),p(ee.$$.fragment,e),Fe=a(e),p(te.$$.fragment,e),Ee=a(e),ie=r(e,"P",{"data-svelte-h":!0}),m(ie)!=="svelte-xy3pkj"&&(ie.innerHTML=vt),De=a(e),p(le.$$.fragment,e),qe=a(e),pe=r(e,"P",{}),tt(pe).forEach(i),this.h()},h(){me(g,"name","hf:doc:metadata"),me(g,"content",Gt),_t(h.src,rt="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-pi0%20(1).png")||me(h,"src",rt),me(h,"alt","An overview of Pi0"),me(h,"width","85%")},m(e,t){se(document.head,g),l(e,ce,t),l(e,oe,t),l(e,de,t),c($,e,t),l(e,fe,t),c(w,e,t),l(e,ue,t),l(e,b,t),l(e,ye,t),c(T,e,t),l(e,ge,t),l(e,U,t),l(e,he,t),l(e,h,t),l(e,Me,t),c(C,e,t),l(e,$e,t),l(e,v,t),l(e,we,t),c(J,e,t),l(e,be,t),l(e,_,t),l(e,Te,t),l(e,x,t),l(e,Ue,t),c(I,e,t),l(e,Ce,t),l(e,M,t),se(M,ne),se(M,it),se(M,L),se(L,ae),se(L,lt),c(j,L,null),l(e,ve,t),c(R,e,t),l(e,Je,t),l(e,V,t),l(e,_e,t),l(e,X,t),l(e,xe,t),c(Z,e,t),l(e,Ie,t),l(e,G,t),l(e,Le,t),c(k,e,t),l(e,je,t),c(Q,e,t),l(e,Re,t),l(e,B,t),l(e,Ve,t),c(H,e,t),l(e,Xe,t),c(z,e,t),l(e,Ze,t),l(e,P,t),l(e,Ge,t),c(S,e,t),l(e,ke,t),l(e,W,t),l(e,Qe,t),l(e,Y,t),l(e,Be,t),c(A,e,t),l(e,He,t),l(e,N,t),l(e,ze,t),l(e,F,t),l(e,Pe,t),c(E,e,t),l(e,Se,t),l(e,D,t),l(e,We,t),c(q,e,t),l(e,Ye,t),l(e,O,t),l(e,Ae,t),l(e,K,t),l(e,Ne,t),c(ee,e,t),l(e,Fe,t),c(te,e,t),l(e,Ee,t),l(e,ie,t),l(e,De,t),c(le,e,t),l(e,qe,t),l(e,pe,t),Oe=!0},p:xt,i(e){Oe||(d($.$$.fragment,e),d(w.$$.fragment,e),d(T.$$.fragment,e),d(C.$$.fragment,e),d(J.$$.fragment,e),d(I.$$.fragment,e),d(j.$$.fragment,e),d(R.$$.fragment,e),d(Z.$$.fragment,e),d(k.$$.fragment,e),d(Q.$$.fragment,e),d(H.$$.fragment,e),d(z.$$.fragment,e),d(S.$$.fragment,e),d(A.$$.fragment,e),d(E.$$.fragment,e),d(q.$$.fragment,e),d(ee.$$.fragment,e),d(te.$$.fragment,e),d(le.$$.fragment,e),Oe=!0)},o(e){f($.$$.fragment,e),f(w.$$.fragment,e),f(T.$$.fragment,e),f(C.$$.fragment,e),f(J.$$.fragment,e),f(I.$$.fragment,e),f(j.$$.fragment,e),f(R.$$.fragment,e),f(Z.$$.fragment,e),f(k.$$.fragment,e),f(Q.$$.fragment,e),f(H.$$.fragment,e),f(z.$$.fragment,e),f(S.$$.fragment,e),f(A.$$.fragment,e),f(E.$$.fragment,e),f(q.$$.fragment,e),f(ee.$$.fragment,e),f(te.$$.fragment,e),f(le.$$.fragment,e),Oe=!1},d(e){e&&(i(ce),i(oe),i(de),i(fe),i(ue),i(b),i(ye),i(ge),i(U),i(he),i(h),i(Me),i($e),i(v),i(we),i(be),i(_),i(Te),i(x),i(Ue),i(Ce),i(M),i(ve),i(Je),i(V),i(_e),i(X),i(xe),i(Ie),i(G),i(Le),i(je),i(Re),i(B),i(Ve),i(Xe),i(Ze),i(P),i(Ge),i(ke),i(W),i(Qe),i(Y),i(Be),i(He),i(N),i(ze),i(F),i(Pe),i(Se),i(D),i(We),i(Ye),i(O),i(Ae),i(K),i(Ne),i(Fe),i(Ee),i(ie),i(De),i(qe),i(pe)),i(g),u($,e),u(w,e),u(T,e),u(C,e),u(J,e),u(I,e),u(j),u(R,e),u(Z,e),u(k,e),u(Q,e),u(H,e),u(z,e),u(S,e),u(A,e),u(E,e),u(q,e),u(ee,e),u(te,e),u(le,e)}}}const Gt='{"title":"π₀ (Pi0)","local":"π₀-pi0","sections":[{"title":"Model Overview","local":"model-overview","sections":[{"title":"The Vision for Physical Intelligence","local":"the-vision-for-physical-intelligence","sections":[],"depth":3},{"title":"Architecture and Approach","local":"architecture-and-approach","sections":[],"depth":3}],"depth":2},{"title":"Installation Requirements","local":"installation-requirements","sections":[],"depth":2},{"title":"Training Data and Capabilities","local":"training-data-and-capabilities","sections":[],"depth":2},{"title":"Usage","local":"usage","sections":[],"depth":2},{"title":"Training","local":"training","sections":[{"title":"Key Training Parameters","local":"key-training-parameters","sections":[],"depth":3},{"title":"Training Parameters Explained","local":"training-parameters-explained","sections":[],"depth":3}],"depth":2},{"title":"Relative Actions","local":"relative-actions","sections":[],"depth":2},{"title":"License","local":"license","sections":[],"depth":2}],"depth":1}';function kt(nt){return It(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class St extends Lt{constructor(g){super(),jt(this,g,kt,Zt,Jt,{})}}export{St as component};
