• Unitree G1-D Ultimate C Humanoid Robot Platform (Dex3 Hands w/ Feedback)
• Multi-sensor perception system
• Secondary development supported; visualization computer included
• Wheel-base dual-arm humanoid robot
• Dex3-1 force-controlled three-finger dexterous hands (w/ RGB Camera & tactile feedback)
• Total degrees of freedom: 33

The Unitree G1-D Ultimate C Humanoid Robot Platform (Dex3 Hands w/ Feedback) is the wheel-base G1-D Ultimate C configuration that pairs the Ultimate A chassis with Dex3-1 three-finger dexterous hands with tactile feedback (33 tactile sensors, 10 g to 2500 g perception range), built for mobile manipulation workflows that benefit from touch sensing.

The G1-D platform is built around a full-size humanoid upper body with a 17-DOF kinematic structure (excluding the end effector), a vertical lifting column with a 500 mm stroke for adjustable working height, and an integrated multi-sensor perception stack (head-mounted binocular vision plus wrist cameras) to support manipulation and teleoperation workflows; it is supplied with the standard control electronics/software stack and the required power/charging interface for deployment. As an authorized reseller with local support and logistics, Robqo acts as a single point of contact for purchase and after-sales service, reducing friction versus a direct-from-manufacturer purchase and avoiding the need to ship the robot abroad for support or resolution.

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/* key bits */
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/* FIX: images can create extra baseline space; force block + no baseline */
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/* 2 cols on tablet */
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Why Buy From Robqo

Official Authorized Distributor

Genuine products with manufacturer-approved warranty coverage.

Locally Managed Logistics

Customs, inspection, and prep handled locally before shipment.

Single Point of Contact

Sales, support, and returns managed by one team.

FAQ & Knowledge Base

Docs, tutorials, and community answers to move faster.

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Core Building Blocks

High-Output Humanoid Robot

Proprietary humanoid platforms built end-to-end with in-house actuators, gearsets, encoders, and integrated sensing.

Efficient Data Collection Suite

A single workflow covering the entire data lifecycle: capture, processing, annotation, validation, and dataset asset management.

End-to-End Training & Inference Toolkit

Supports distributed training, custom model creation, and smooth deployment, with compatibility across leading open-source frameworks.

Expanded Working
Envelope →

Expanded Working
Envelope

Mobile Operation: Uses a mobile lifting architecture that integrates wheels with a lifting mechanism

Vertical Workspace: 0–2m

Waist ROM (Z): ±155°

Waist ROM (Y): -2.5° ~ +135°

Faster, Low-Latency
Control →

Faster, Low-Latency
Control

Lifting Accuracy: ±0.5mm

End-Effector Gripper Accuracy: ±0.1mm

Note: Accuracy can vary depending on the end-effector configuration.

System Teleoperation Latency: <100ms

Sampling Rate: 60Hz

High-DOF Robot
Platform →

High-DOF Robot
Platform

Total Degrees of Freedom

Robot DOF (Excl. End-Effector): 19

Arm Degrees of Freedom: 7×2

Waist Degrees of Freedom: 2

Column Degrees of Freedom: 1

Base Degrees of Freedom: 2

(for motorized based only)

/* =========================================================
3-pane (MFG-style) — isolated vars, no H tags
========================================================= */

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Optimized Data Acquisition Toolkit

Accelerate data collection and lower operating costs with flexible, standardized workflows — replacing slow, manual methods with a repeatable process.

Template-Driven Workflows for Faster Collection

Combines project coordination, task assignment, progress monitoring, and status reporting into one workspace. Using reusable templates, collection tasks can be created in a single click. Real-time visibility across the pipeline improves collaboration and keeps data acquisition running smoothly.

Flexible Setups Across Platforms & End Effectors

Supports data capture across multiple robot platforms and end-effector configurations. Strong standardization turns inputs from diverse devices into consistent, high-quality training-ready datasets through an end-to-end workflow.

Scalable, High-Concurrency Architecture

Designed to coordinate hundreds of robots performing synchronized data collection. With high-concurrency services and load-balanced scheduling, the platform reliably ingests and processes large, real-time data streams.

Always-On Collection, Stable & Reliable

Built on a highly available service foundation to support dependable 24/7 collection. Broad format compatibility lets outputs be used directly or converted into widely adopted training formats, speeding up R&D cycles.

/* =========================================================
Streamlined Data Acquisition Tools (scoped + WYSIWYG-safe)
– No H tags
– All vars prefixed: –g1d-daq-*
========================================================= */

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background:var(–g1d-daq-area-bg);
border-radius:var(–g1d-daq-area-radius);
padding:var(–g1d-daq-pad-y) var(–g1d-daq-pad-x);
}

.unitree-g1-d-data-aquisition-tools .daq-wrap{ max-width:var(–g1d-daq-maxw); margin:0 auto; }

.unitree-g1-d-data-aquisition-tools .daq-head{
text-align:center;
margin:0 0 clamp(18px,2.4vw,28px) 0;
}
.unitree-g1-d-data-aquisition-tools .daq-head-title{
margin:0;
color:var(–g1d-daq-title);
font-weight:750;
letter-spacing:.2px;
line-height:1.18;
font-size:clamp(22px,2.6vw,34px);
}
.unitree-g1-d-data-aquisition-tools .daq-head p{
margin:10px auto 0;
max-width:860px;
color:var(–g1d-daq-sub);
font-size:clamp(13px,1.25vw,15px);
line-height:1.6;
}

.unitree-g1-d-data-aquisition-tools .daq-grid{
display:grid;
gap:var(–g1d-daq-grid-gap);
grid-template-columns:repeat(2, minmax(0, 1fr));
}

.unitree-g1-d-data-aquisition-tools .daq-card{
background:var(–g1d-daq-card-bg);
border:1px solid var(–g1d-daq-card-bd);
border-radius:var(–g1d-daq-card-radius);
box-shadow:var(–g1d-daq-card-shadow);
padding:var(–g1d-daq-card-pad);
min-height:140px;
}

.unitree-g1-d-data-aquisition-tools .daq-icon{
width:var(–g1d-daq-icon-box);
height:var(–g1d-daq-icon-box);
border-radius:var(–g1d-daq-icon-radius);
background:var(–g1d-daq-icon-bg);
display:flex;
align-items:center;
justify-content:center;
margin:0 0 12px 0;
}
.unitree-g1-d-data-aquisition-tools .daq-icon img{
width:var(–g1d-daq-icon-img) !important;
height:var(–g1d-daq-icon-img) !important;
display:block;
}

.unitree-g1-d-data-aquisition-tools .daq-title{
margin:0 0 10px 0;
color:var(–g1d-daq-title);
font-weight:750;
font-size:16px;
line-height:1.25;
}
.unitree-g1-d-data-aquisition-tools .daq-body{
margin:0;
color:var(–g1d-daq-sub);
font-size:13.5px;
line-height:1.6;
}

@media (max-width:768px){
.unitree-g1-d-data-aquisition-tools{ padding:26px 16px; }
.unitree-g1-d-data-aquisition-tools .daq-grid{ grid-template-columns:1fr; }
.unitree-g1-d-data-aquisition-tools .daq-card{ padding:var(–g1d-daq-card-pad-m); }
}

#g1d-data-pipeline{
width:100%;
background:var(–g1ddp-bg);
padding:var(–g1ddp-pad-y) var(–g1ddp-pad-x);
border-radius:var(–g1ddp-radius);
overflow:hidden;
box-sizing:border-box;
}
#g1d-data-pipeline, #g1d-data-pipeline *{ box-sizing:border-box; }

#g1d-data-pipeline .rs-dp-inner{
max-width:var(–g1ddp-inner-max);
margin:0 auto;
font-family:system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;
color:var(–g1ddp-text);
}

#g1d-data-pipeline .rs-dp-titlewrap{ text-align:center; margin:0 0 18px; }
#g1d-data-pipeline .rs-dp-title{
display:inline-block;
background:var(–g1ddp-accent);
color:#fff;
font-weight:700;
font-size:14px;
line-height:1;
padding:8px 14px;
border-radius:999px;
}

#g1d-data-pipeline .rs-dp-radio{
position:absolute;
left:-9999px;
opacity:0;
pointer-events:none;
}

#g1d-data-pipeline .rs-dp-steps{
list-style:none;
display:flex;
justify-content:space-between;
align-items:flex-start;
gap:10px;
padding:0;
margin:0 auto 18px;
position:relative;
max-width:980px;
}
/* dotted connector line (like the screenshot) */
#g1d-data-pipeline .rs-dp-steps::before{
content:””;
position:absolute;
left:4%;
right:4%;
top:14px;
height:0;
border-top:2px dashed var(–g1ddp-line);
z-index:0;
}

#g1d-data-pipeline .rs-dp-step{ flex:1; min-width:0; text-align:center; position:relative; z-index:1; }
#g1d-data-pipeline .rs-dp-dot{
width:28px;height:28px;border-radius:999px;
margin:0 auto 6px;
display:grid;place-items:center;
font-weight:800;font-size:13px;
color:#fff;
background:var(–g1ddp-muted);
box-shadow:0 1px 0 rgba(0,0,0,.08);
}
#g1d-data-pipeline .rs-dp-label{
font-size:12px;
line-height:1.15;
color:var(–g1ddp-muted);
font-weight:600;
}

#g1d-data-pipeline .rs-dp-stage{ position:relative; max-width:var(–g1ddp-imgw); margin:0 auto; }
#g1d-data-pipeline .rs-dp-imgwrap{ position:relative; width:100%; margin:0 auto; }
#g1d-data-pipeline .rs-dp-imgwrap::before{ content:””; display:block; padding-top:var(–g1ddp-imgH); }

#g1d-data-pipeline .rs-dp-imgwrap img{
position:absolute; inset:0;
width:100%; height:100%;
object-fit:contain;
opacity:0;
transition:opacity .18s ease-in-out;
will-change:opacity;
}

#g1d-data-pipeline .rs-dp-arrow{
position:absolute;
top:50%;
transform:translateY(-50%);
width:var(–g1ddp-arrow);
height:var(–g1ddp-arrow);
border-radius:999px;
border:1px solid var(–g1ddp-line);
background:#fff;
box-shadow:0 1px 0 rgba(0,0,0,.06);
display:none;
align-items:center;
justify-content:center;
cursor:pointer;
user-select:none;
z-index:3;
}
#g1d-data-pipeline .rs-dp-arrow span{ font-size:22px; line-height:1; color:#111; }
#g1d-data-pipeline .rs-dp-arrow:hover{ filter:brightness(.98); }
#g1d-data-pipeline .rs-dp-arrow:active{ transform:translateY(-50%) scale(.98); }
#g1d-data-pipeline .rs-dp-arrow.prev{ left:-58px; }
#g1d-data-pipeline .rs-dp-arrow.next{ right:-58px; }

/* Active highlight */
#g1d-data-pipeline #g1ddp-s1:checked ~ .rs-dp-steps .s1 .rs-dp-dot,
#g1d-data-pipeline #g1ddp-s2:checked ~ .rs-dp-steps .s2 .rs-dp-dot,
#g1d-data-pipeline #g1ddp-s3:checked ~ .rs-dp-steps .s3 .rs-dp-dot,
#g1d-data-pipeline #g1ddp-s4:checked ~ .rs-dp-steps .s4 .rs-dp-dot,
#g1d-data-pipeline #g1ddp-s5:checked ~ .rs-dp-steps .s5 .rs-dp-dot,
#g1d-data-pipeline #g1ddp-s6:checked ~ .rs-dp-steps .s6 .rs-dp-dot{ background:var(–g1ddp-accent); }

#g1d-data-pipeline #g1ddp-s1:checked ~ .rs-dp-steps .s1 .rs-dp-label,
#g1d-data-pipeline #g1ddp-s2:checked ~ .rs-dp-steps .s2 .rs-dp-label,
#g1d-data-pipeline #g1ddp-s3:checked ~ .rs-dp-steps .s3 .rs-dp-label,
#g1d-data-pipeline #g1ddp-s4:checked ~ .rs-dp-steps .s4 .rs-dp-label,
#g1d-data-pipeline #g1ddp-s5:checked ~ .rs-dp-steps .s5 .rs-dp-label,
#g1d-data-pipeline #g1ddp-s6:checked ~ .rs-dp-steps .s6 .rs-dp-label{ color:var(–g1ddp-text); }

/* Show selected image */
#g1d-data-pipeline #g1ddp-s1:checked ~ .rs-dp-stage .img1{ opacity:1; }
#g1d-data-pipeline #g1ddp-s2:checked ~ .rs-dp-stage .img2{ opacity:1; }
#g1d-data-pipeline #g1ddp-s3:checked ~ .rs-dp-stage .img3{ opacity:1; }
#g1d-data-pipeline #g1ddp-s4:checked ~ .rs-dp-stage .img4{ opacity:1; }
#g1d-data-pipeline #g1ddp-s5:checked ~ .rs-dp-stage .img5{ opacity:1; }
#g1d-data-pipeline #g1ddp-s6:checked ~ .rs-dp-stage .img6{ opacity:1; }

/* Show correct arrows */
#g1d-data-pipeline #g1ddp-s1:checked ~ .rs-dp-stage .prev.p1,
#g1d-data-pipeline #g1ddp-s1:checked ~ .rs-dp-stage .next.n1{ display:flex; }
#g1d-data-pipeline #g1ddp-s2:checked ~ .rs-dp-stage .prev.p2,
#g1d-data-pipeline #g1ddp-s2:checked ~ .rs-dp-stage .next.n2{ display:flex; }
#g1d-data-pipeline #g1ddp-s3:checked ~ .rs-dp-stage .prev.p3,
#g1d-data-pipeline #g1ddp-s3:checked ~ .rs-dp-stage .next.n3{ display:flex; }
#g1d-data-pipeline #g1ddp-s4:checked ~ .rs-dp-stage .prev.p4,
#g1d-data-pipeline #g1ddp-s4:checked ~ .rs-dp-stage .next.n4{ display:flex; }
#g1d-data-pipeline #g1ddp-s5:checked ~ .rs-dp-stage .prev.p5,
#g1d-data-pipeline #g1ddp-s5:checked ~ .rs-dp-stage .next.n5{ display:flex; }
#g1d-data-pipeline #g1ddp-s6:checked ~ .rs-dp-stage .prev.p6,
#g1d-data-pipeline #g1ddp-s6:checked ~ .rs-dp-stage .next.n6{ display:flex; }

@media (max-width:720px){
#g1d-data-pipeline .rs-dp-steps{ gap:6px; }
#g1d-data-pipeline .rs-dp-label{ font-size:11px; }
#g1d-data-pipeline .rs-dp-arrow.prev{ left:8px; }
#g1d-data-pipeline .rs-dp-arrow.next{ right:8px; }
}

Data Collection Pipeline

1. 1
   Create New
   Data Collection
   Tasks
2. 2
   Task Updates
   and
   Assignment
3. 3
   Data Capture
   and
   Annotation
4. 4
   Upload Data
   and Review
5. 5
   Data Archiving
6. 6
   Dataset Export

‹ › ‹ › ‹ › ‹ › ‹ › ‹ ›

Full-Stack Model Training & Inference Tools

Covers the complete workflow from data preparation to one-click deployment. The platform integrates smoothly with widely used open-source robotics model frameworks.

Broad Ecosystem with Popular Model Compatibility

An open model ecosystem with built-in community datasets and support for training on open-source datasets. It also provides deep integration with leading open-source models such as PI and GROOT.

Simulation Suite for Fast Model Validation

Includes a high-fidelity, high-precision 3D asset library. By building realistic simulation scenes, it quickly produces comprehensive evaluation plans to support algorithm verification.

Out-of-the-Box Experience for Quick Rollout

Ready to deploy immediately, reducing setup time. Kick off development with “one-click training,” use integrated simulation for reliable evaluation, and transition smoothly from algorithms to real-world machines.

High-Efficiency Distributed Training

Built on a high-performance distributed training stack, enabling elastic task scheduling and parallel acceleration. It scales dynamically with available resources, achieving up to 90% GPU utilization.

/* =========================================================
Training & Inference Tools
– IDENTICAL layout + styling rules as Data Acquisition Tools
– Different namespace vars: –g1dti-*
– No H tags
========================================================= */

.g1-d-training-inference-tools{
background:var(–g1dti-area-bg);
border-radius:var(–g1dti-area-radius);
padding:var(–g1dti-pad-y) var(–g1dti-pad-x);
}

.g1-d-training-inference-tools .ti-wrap{
max-width:var(–g1dti-maxw);
margin:0 auto;
}

.g1-d-training-inference-tools .ti-head{
text-align:center;
margin:0 0 clamp(18px,2.4vw,28px) 0;
}

/* Matches the DAQ block’s heading styling exactly */
.g1-d-training-inference-tools .ti-head-title{
margin:0;
color:var(–g1dti-title);
font-weight:750;
letter-spacing:.2px;
line-height:1.18;
font-size:clamp(22px,2.6vw,34px);
}

/* Matches the DAQ block’s intro paragraph styling exactly */
.g1-d-training-inference-tools .ti-head p{
margin:10px auto 0;
max-width:860px;
color:var(–g1dti-sub);
font-size:clamp(13px,1.25vw,15px);
line-height:1.6;
}

.g1-d-training-inference-tools .ti-grid{
display:grid;
gap:var(–g1dti-grid-gap);
grid-template-columns:repeat(2, minmax(0, 1fr));
}

.g1-d-training-inference-tools .ti-card{
background:var(–g1dti-card-bg);
border:1px solid var(–g1dti-card-bd);
border-radius:var(–g1dti-card-radius);
box-shadow:var(–g1dti-card-shadow);
padding:var(–g1dti-card-pad);
min-height:140px;
}

/* Matches DAQ icon box */
.g1-d-training-inference-tools .ti-icon{
width:var(–g1dti-icon-box);
height:var(–g1dti-icon-box);
border-radius:var(–g1dti-icon-radius);
background:var(–g1dti-icon-bg);
display:flex;
align-items:center;
justify-content:center;
margin:0 0 12px 0;
}

.g1-d-training-inference-tools .ti-icon img{
width:var(–g1dti-icon-img) !important;
height:var(–g1dti-icon-img) !important;
display:block;
}

/* Matches DAQ title/body typography */
.g1-d-training-inference-tools .ti-title{
margin:0 0 10px 0;
color:var(–g1dti-title);
font-weight:750;
font-size:16px;
line-height:1.25;
}

.g1-d-training-inference-tools .ti-body{
margin:0;
color:var(–g1dti-sub);
font-size:13.5px;
line-height:1.6;
}

@media (max-width:768px){
.g1-d-training-inference-tools{ padding:26px 16px; }
.g1-d-training-inference-tools .ti-grid{ grid-template-columns:1fr; }
.g1-d-training-inference-tools .ti-card{ padding:var(–g1dti-card-pad-m); }
}

#g1d-inference-pipeline{
width:100%;
background:var(–g1dinf-bg);
padding:var(–g1dinf-pad-y) var(–g1dinf-pad-x);
border-radius:var(–g1dinf-radius);
overflow:hidden;
box-sizing:border-box;
}
#g1d-inference-pipeline, #g1d-inference-pipeline *{ box-sizing:border-box; }

#g1d-inference-pipeline .rs-inf-inner{
max-width:var(–g1dinf-inner-max);
margin:0 auto;
font-family:system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;
color:var(–g1dinf-text);
}

#g1d-inference-pipeline .rs-inf-titlewrap{ text-align:center; margin:0 0 18px; }
#g1d-inference-pipeline .rs-inf-title{
display:inline-block;
background:var(–g1dinf-accent);
color:#fff;
font-weight:700;
font-size:14px;
line-height:1;
padding:8px 14px;
border-radius:999px;
}

#g1d-inference-pipeline .rs-inf-radio{
position:absolute;
left:-9999px;
opacity:0;
pointer-events:none;
}

#g1d-inference-pipeline .rs-inf-steps{
list-style:none;
display:flex;
justify-content:space-between;
align-items:flex-start;
gap:10px;
padding:0;
margin:0 auto 18px;
position:relative;
max-width:980px;
}
/* dotted connector line (MATCH data-pipeline) */
#g1d-inference-pipeline .rs-inf-steps::before{
content:””;
position:absolute;
left:4%;
right:4%;
top:14px;
height:0;
border-top:2px dashed var(–g1dinf-line);
z-index:0;
}

#g1d-inference-pipeline .rs-inf-step{ flex:1; min-width:0; text-align:center; position:relative; z-index:1; }
#g1d-inference-pipeline .rs-inf-dot{
width:28px;height:28px;border-radius:999px;
margin:0 auto 6px;
display:grid;place-items:center;
font-weight:800;font-size:13px;
color:#fff;
background:var(–g1dinf-muted);
box-shadow:0 1px 0 rgba(0,0,0,.08);
}
#g1d-inference-pipeline .rs-inf-label{
font-size:12px;
line-height:1.15;
color:var(–g1dinf-muted);
font-weight:600;
}

#g1d-inference-pipeline .rs-inf-stage{ position:relative; max-width:var(–g1dinf-imgw); margin:0 auto; }
#g1d-inference-pipeline .rs-inf-imgwrap{ position:relative; width:100%; margin:0 auto; }
#g1d-inference-pipeline .rs-inf-imgwrap::before{ content:””; display:block; padding-top:var(–g1dinf-imgH); }

#g1d-inference-pipeline .rs-inf-imgwrap img{
position:absolute; inset:0;
width:100%; height:100%;
object-fit:contain;
opacity:0;
transition:opacity .18s ease-in-out;
will-change:opacity;
}

#g1d-inference-pipeline .rs-inf-arrow{
position:absolute;
top:50%;
transform:translateY(-50%);
width:var(–g1dinf-arrow);
height:var(–g1dinf-arrow);
border-radius:999px;
border:1px solid var(–g1dinf-line);
background:#fff;
box-shadow:0 1px 0 rgba(0,0,0,.06);
display:none;
align-items:center;
justify-content:center;
cursor:pointer;
user-select:none;
z-index:3;
}
#g1d-inference-pipeline .rs-inf-arrow span{ font-size:22px; line-height:1; color:#111; }
#g1d-inference-pipeline .rs-inf-arrow:hover{ filter:brightness(.98); }
#g1d-inference-pipeline .rs-inf-arrow:active{ transform:translateY(-50%) scale(.98); }
#g1d-inference-pipeline .rs-inf-arrow.prev{ left:-58px; }
#g1d-inference-pipeline .rs-inf-arrow.next{ right:-58px; }

/* Active highlight (unique IDs) */
#g1d-inference-pipeline #g1dinf-s1:checked ~ .rs-inf-steps .s1 .rs-inf-dot,
#g1d-inference-pipeline #g1dinf-s2:checked ~ .rs-inf-steps .s2 .rs-inf-dot,
#g1d-inference-pipeline #g1dinf-s3:checked ~ .rs-inf-steps .s3 .rs-inf-dot,
#g1d-inference-pipeline #g1dinf-s4:checked ~ .rs-inf-steps .s4 .rs-inf-dot,
#g1d-inference-pipeline #g1dinf-s5:checked ~ .rs-inf-steps .s5 .rs-inf-dot,
#g1d-inference-pipeline #g1dinf-s6:checked ~ .rs-inf-steps .s6 .rs-inf-dot{ background:var(–g1dinf-accent); }

#g1d-inference-pipeline #g1dinf-s1:checked ~ .rs-inf-steps .s1 .rs-inf-label,
#g1d-inference-pipeline #g1dinf-s2:checked ~ .rs-inf-steps .s2 .rs-inf-label,
#g1d-inference-pipeline #g1dinf-s3:checked ~ .rs-inf-steps .s3 .rs-inf-label,
#g1d-inference-pipeline #g1dinf-s4:checked ~ .rs-inf-steps .s4 .rs-inf-label,
#g1d-inference-pipeline #g1dinf-s5:checked ~ .rs-inf-steps .s5 .rs-inf-label,
#g1d-inference-pipeline #g1dinf-s6:checked ~ .rs-inf-steps .s6 .rs-inf-label{ color:var(–g1dinf-text); }

/* Show selected image */
#g1d-inference-pipeline #g1dinf-s1:checked ~ .rs-inf-stage .img1{ opacity:1; }
#g1d-inference-pipeline #g1dinf-s2:checked ~ .rs-inf-stage .img2{ opacity:1; }
#g1d-inference-pipeline #g1dinf-s3:checked ~ .rs-inf-stage .img3{ opacity:1; }
#g1d-inference-pipeline #g1dinf-s4:checked ~ .rs-inf-stage .img4{ opacity:1; }
#g1d-inference-pipeline #g1dinf-s5:checked ~ .rs-inf-stage .img5{ opacity:1; }
#g1d-inference-pipeline #g1dinf-s6:checked ~ .rs-inf-stage .img6{ opacity:1; }

/* Show correct arrows */
#g1d-inference-pipeline #g1dinf-s1:checked ~ .rs-inf-stage .prev.p1,
#g1d-inference-pipeline #g1dinf-s1:checked ~ .rs-inf-stage .next.n1{ display:flex; }
#g1d-inference-pipeline #g1dinf-s2:checked ~ .rs-inf-stage .prev.p2,
#g1d-inference-pipeline #g1dinf-s2:checked ~ .rs-inf-stage .next.n2{ display:flex; }
#g1d-inference-pipeline #g1dinf-s3:checked ~ .rs-inf-stage .prev.p3,
#g1d-inference-pipeline #g1dinf-s3:checked ~ .rs-inf-stage .next.n3{ display:flex; }
#g1d-inference-pipeline #g1dinf-s4:checked ~ .rs-inf-stage .prev.p4,
#g1d-inference-pipeline #g1dinf-s4:checked ~ .rs-inf-stage .next.n4{ display:flex; }
#g1d-inference-pipeline #g1dinf-s5:checked ~ .rs-inf-stage .prev.p5,
#g1d-inference-pipeline #g1dinf-s5:checked ~ .rs-inf-stage .next.n5{ display:flex; }
#g1d-inference-pipeline #g1dinf-s6:checked ~ .rs-inf-stage .prev.p6,
#g1d-inference-pipeline #g1dinf-s6:checked ~ .rs-inf-stage .next.n6{ display:flex; }

@media (max-width:720px){
#g1d-inference-pipeline .rs-inf-steps{ gap:6px; }
#g1d-inference-pipeline .rs-inf-label{ font-size:11px; }
#g1d-inference-pipeline .rs-inf-arrow.prev{ left:8px; }
#g1d-inference-pipeline .rs-inf-arrow.next{ right:8px; }
}

Model Training & Deployment Workflow

1. 1
   Choose
   Model
   Architecture
2. 2
   Set Training
   Configuration
3. 3
   Live Training
   Monitoring
4. 4
   Parameter
   Tuning
5. 5
   Run Simulation
   Tests
6. 6
   Export & Deploy
   Model

‹ › ‹ › ‹ › ‹ › ‹ › ‹ ›

Application Scenario

Service

Life

Retail

Industry

/* =========================================================
Application Scenario (TRICKY FIX KEPT)
– No “dead space” when hovering gaps (flex-grow weights)
– Variables are block-prefixed: –g1dappsc-*
– Selectors remain scoped under .unitree-g1-d-application-scenario
========================================================= */

.unitree-g1-d-application-scenario .rs-appsc-title{
margin:28px 0 18px;
text-align:center;
font:500 clamp(28px, 3.2vw, 44px)/1.15 system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;
color:var(–g1dappsc-title, #111) !important;
}

.unitree-g1-d-application-scenario .rs-appsc-panels{
display:flex !important;
gap:var(–g1dappsc-gap, 10px);
width:100%;
height:var(–g1dappsc-h, 360px);
align-items:stretch;
min-width:0;
line-height:0 !important;
}

.unitree-g1-d-application-scenario .rs-appsc-panel{
position:relative;
min-width:0;
overflow:hidden;
border-radius:var(–g1dappsc-radius, 15px);

height:100%;
display:block;
box-sizing:border-box;
isolation:isolate;

/* IMPORTANT: allow flex to fill remaining space (prevents gaps/blank area) */
flex:1 1 0%;

margin:0 !important;
padding:0 !important;
border:0 !important;
outline:0 !important;
background:transparent !important;
box-shadow:none !important;
background-image:none !important;
}

.unitree-g1-d-application-scenario .rs-appsc-img{
position:absolute;
inset:0 !important;
width:100% !important;
height:100% !important;
display:block !important;
margin:0 !important;
padding:0 !important;
border:0 !important;

object-fit:cover;
pointer-events:none;

transform:scale(1) !important;
transition:transform 600ms ease;
}

.unitree-g1-d-application-scenario .rs-appsc-panel::after{
content:””;
position:absolute;
inset:0;
z-index:1;
pointer-events:none;

background:linear-gradient(90deg, rgba(0,0,0,.55), rgba(0,0,0,.15));
opacity:.75;
transition:opacity 420ms ease;
}

.unitree-g1-d-application-scenario .rs-appsc-label{
position:absolute;
top:14px;
left:14px;
z-index:2;
pointer-events:none;

margin:0 !important;
font:600 22px/1.1 system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;
color:#fff !important;
text-shadow:0 2px 14px rgba(0,0,0,.55);
}

/* Desktop hover-expand (NO blank space when mouse is in the gap) */
@media (hover:hover) and (pointer:fine){
.unitree-g1-d-application-scenario .rs-appsc-panel{
/* smooth width changes */
transition:flex-grow 420ms ease, filter 420ms ease !important;
will-change:flex-grow;
}

/* When hovering the whole row: dim all panels, but KEEP widths filling 100% */
.unitree-g1-d-application-scenario .rs-appsc-panels:hover .rs-appsc-panel{
filter:brightness(.55);
flex-grow:3; /* baseline weight */
}

/* Hovered panel gets bigger (11 / (11+3+3+3) = 55%) */
.unitree-g1-d-application-scenario .rs-appsc-panels:hover .rs-appsc-panel:hover{
filter:brightness(1);
flex-grow:11;
}

.unitree-g1-d-application-scenario .rs-appsc-panels:hover .rs-appsc-panel:hover::after{
opacity:.35;
}

/* Zoom toggle (was style*=”–rs-appsc-zoom:X”) */
.unitree-g1-d-application-scenario[style*=”–g1dappsc-zoom:1″] .rs-appsc-panels:hover .rs-appsc-panel:hover .rs-appsc-img{
transform:scale(1.08) !important;
}
.unitree-g1-d-application-scenario[style*=”–g1dappsc-zoom:0″] .rs-appsc-panels:hover .rs-appsc-panel:hover .rs-appsc-img{
transform:scale(1) !important;
}
}

/* Responsive layout */
@media (max-width:820px){
.unitree-g1-d-application-scenario .rs-appsc-panels{
display:grid !important;
grid-template-columns:1fr 1fr;
height:auto;
line-height:normal !important;
}
.unitree-g1-d-application-scenario .rs-appsc-panel{
height:auto;
min-height:170px;
}
}

@media (max-width:520px){
.unitree-g1-d-application-scenario .rs-appsc-panels{ grid-template-columns:1fr; }
.unitree-g1-d-application-scenario .rs-appsc-panel{ min-height:200px; }
}

@media (prefers-reduced-motion:reduce){
.unitree-g1-d-application-scenario .rs-appsc-panel,
.unitree-g1-d-application-scenario .rs-appsc-img,
.unitree-g1-d-application-scenario .rs-appsc-panel::after{ transition:none !important; }
}

.rs-section.initree-g1-d-world-model-action{
/* Match your other light blocks (gray background, no outer border) */
–g1d-wma-bg:#f8f8f8;
–g1d-wma-ink:#0b1220;
–g1d-wma-muted:rgba(11,18,32,.62);

–g1d-wma-radius:15px;
–g1d-wma-pad:16px;

–g1d-wma-gap:26px;
–g1d-wma-gap-sm:16px;

–g1d-wma-hero-title:clamp(22px,3.2vw,34px);
–g1d-wma-hero-lead:clamp(13px,1.45vw,15px);

–g1d-wma-h3:clamp(18px,2.2vw,26px);
–g1d-wma-body:14px;

–g1d-wma-media-radius:14px;

/* NEW: content max width (background stays full width) */
–g1d-wma-max:1140px;

font-family:system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;
color:var(–g1d-wma-ink);
background:transparent;
width:100%;
}
.rs-section.initree-g1-d-world-model-action,
.rs-section.initree-g1-d-world-model-action *{ box-sizing:border-box; }

/* Live theme neutralizers (scoped) */
.rs-section.initree-g1-d-world-model-action hr{ display:none !important; }
.rs-section.initree-g1-d-world-model-action table,
.rs-section.initree-g1-d-world-model-action tbody,
.rs-section.initree-g1-d-world-model-action tr,
.rs-section.initree-g1-d-world-model-action td{
border:0 !important;
box-shadow:none !important;
background-image:none !important;
outline:0 !important;
border-collapse:collapse !important;
border-spacing:0 !important;
}

/* Background container stays full width */
.rs-section.initree-g1-d-world-model-action .g1d-wma-wrap{
background:var(–g1d-wma-bg);
border:0 !important;
box-shadow:none !important;
border-radius:var(–g1d-wma-radius);
overflow:hidden;
width:100%;
}

/* Center ONLY the content */
.rs-section.initree-g1-d-world-model-action .g1d-wma-inner{
max-width:var(–g1d-wma-max);
margin-left:auto;
margin-right:auto;
padding:var(–g1d-wma-pad);
}

/* Centered hero block */
.rs-section.initree-g1-d-world-model-action .g1d-wma-hero{
text-align:center;
padding:2px 4px 10px 4px;
border-bottom:1px solid rgba(11,18,32,.10);
margin:0 0 var(–g1d-wma-gap) 0;
}
.rs-section.initree-g1-d-world-model-action .g1d-wma-heroTitle{
margin:0 !important;
font-weight:950;
letter-spacing:.2px;
font-size:var(–g1d-wma-hero-title);
line-height:1.12;
color:var(–g1d-wma-ink) !important;
}
.rs-section.initree-g1-d-world-model-action .g1d-wma-lead{
margin:10px auto 0 auto !important;
max-width:980px;
font-size:var(–g1d-wma-hero-lead);
line-height:1.45;
color:var(–g1d-wma-muted) !important;
}

.rs-section.initree-g1-d-world-model-action .g1d-wma-rows{
display:flex;
flex-direction:column;
gap:var(–g1d-wma-gap);
}
.rs-section.initree-g1-d-world-model-action .g1d-wma-row{
display:flex;
align-items:center;
justify-content:space-between;
gap:var(–g1d-wma-gap);
}
.rs-section.initree-g1-d-world-model-action .g1d-wma-row[data-order=”reverse”]{
flex-direction:row-reverse;
}

.rs-section.initree-g1-d-world-model-action .g1d-wma-text{
flex:1 1 44%;
min-width:0;
}
.rs-section.initree-g1-d-world-model-action .g1d-wma-h3{
margin:0 !important;
font-weight:950;
font-size:var(–g1d-wma-h3);
line-height:1.12;
letter-spacing:.2px;
color:var(–g1d-wma-ink) !important;
}
.rs-section.initree-g1-d-world-model-action .g1d-wma-p{
margin:10px 0 0 0 !important;
font-size:var(–g1d-wma-body);
line-height:1.55;
color:var(–g1d-wma-muted) !important;
}

.rs-section.initree-g1-d-world-model-action .g1d-wma-media{
flex:1 1 56%;
min-width:0;
}

/* Media box: 16:9 */
.rs-section.initree-g1-d-world-model-action .g1d-wma-mediaBox{
position:relative;
border-radius:var(–g1d-wma-media-radius);
overflow:hidden;
background:#000;
border:0 !important;
box-shadow:none !important;
line-height:0;
aspect-ratio:16 / 9;
}
.rs-section.initree-g1-d-world-model-action .g1d-wma-img,
.rs-section.initree-g1-d-world-model-action .g1d-wma-video{
position:absolute !important;
inset:0 !important;
display:block !important;
width:100% !important;
height:100% !important;
max-width:100% !important;
border:0 !important;
margin:0 !important;
padding:0 !important;
}
.rs-section.initree-g1-d-world-model-action .g1d-wma-img{ object-fit:cover; }
.rs-section.initree-g1-d-world-model-action .g1d-wma-video{ object-fit:cover; background:#000; }

@media (max-width: 980px){
.rs-section.initree-g1-d-world-model-action .g1d-wma-row{ gap:20px; }
}
@media (max-width: 780px){
.rs-section.initree-g1-d-world-model-action .g1d-wma-inner{ padding:14px; }
.rs-section.initree-g1-d-world-model-action .g1d-wma-row,
.rs-section.initree-g1-d-world-model-action .g1d-wma-row[data-order=”reverse”]{
flex-direction:column;
align-items:stretch;
gap:var(–g1d-wma-gap-sm);
}
}

UnifoLM-WMA-0:
An Open World-Model-Action (WMA) Framework

UnifoLM-WMA-0 is Unitree’s open-source world-model-action architecture across multiple robotic embodiments, built to support general-purpose robot learning. At its core is a world model that learns to interpret physical interactions between robots and the environments they operate in.

Decision-Making Mode:
Action Generation Guided by
Accurate Prediction

Using the current scene state and the task goal, the system forecasts upcoming physical interactions between the robot and its surroundings. These predictions help the policy module select actions, minimizing decision drift while improving the precision and consistency of motion execution.

Simulation Mode:
High-Fidelity Feedback for Data
Synthesis

It can also operate as an interactive simulator, generating high-fidelity environmental feedback from robot motion inputs. By producing realistic synthetic data, it becomes a strong source for model training and policy refinement—helping speed up learning and iteration.