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<title>G1 Moves — Motion Capture for Unitree G1</title>
<meta name="description" content="60 motion capture clips for the Unitree G1 humanoid robot. Dance, karate, and more — captured with MOVIN3D, trained with MuJoCo-Warp.">
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<h1 class="hero-title">G1 MOVES</h1>
<p class="hero-subtitle">60 motion capture clips for the Unitree G1 humanoid robot</p>
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<div class="stage-dot">1</div>
<span class="pipeline-label">Capture</span>
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<span class="pipeline-label">Retarget</span>
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<div class="stage-dot">3</div>
<span class="pipeline-label">Train</span>
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<span class="pipeline-label">Policy</span>
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<button class="category-btn" data-category="dance">Dance</button>
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<h2>ABOUT</h2>
<p class="about-intro">G1 Moves is a dataset of 60 motion capture clips for the <a href="https://www.unitree.com/g1/" target="_blank" rel="noopener">Unitree G1</a> humanoid robot (29 DOF, edition EDU). Each clip flows through a four-stage pipeline — from human performance to autonomous robot motion.</p>
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<div class="process-step">
<div class="process-num">01</div>
<h3>Capture</h3>
<p>Primarily captured with <a href="https://movin3d.com/" target="_blank" rel="noopener">MOVIN TRACIN</a> markerless mocap (LiDAR + vision, 60 FPS, BVH output). Alternatively, <a href="https://github.com/AIM-Intelligence/video2robot" target="_blank" rel="noopener">video2robot</a> extracts motion from any camera source — smartphone video, YouTube, or AI-generated footage.</p>
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<div class="process-num">02</div>
<h3>Retarget</h3>
<p>Per-frame IK via <a href="https://github.com/MOVIN3D/MOVIN-SDK-Python" target="_blank" rel="noopener">MOVIN-SDK-Python</a> maps human skeleton to G1 29-DOF joint limits. <a href="https://github.com/MOVIN3D/Motion-Player-ROS" target="_blank" rel="noopener">Motion-Player-ROS</a> provides real-time preview in RViz.</p>
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<div class="process-num">03</div>
<h3>Train</h3>
<p><a href="https://github.com/mujocolab/mjlab" target="_blank" rel="noopener">mjlab</a> PPO across 8,192 parallel <a href="https://github.com/google-deepmind/mujoco_warp" target="_blank" rel="noopener">MuJoCo-Warp</a> envs. 4-layer MLP actor (160→512→256→128→29). 14-body tracking with adaptive early stopping.</p>
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<div class="process-num">04</div>
<h3>Deploy</h3>
<p><a href="https://github.com/GDDG08/RoboJuDo" target="_blank" rel="noopener">RoboJuDo</a> sim2real framework. MuJoCo sim2sim validation, then deployment to physical G1 via Unitree SDK over Ethernet. Hardware E-stop required.</p>
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<!-- Pipeline Details -->
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<h2>PIPELINE</h2>
<p class="deploy-intro">The complete motion-to-policy pipeline uses six tools. Every trained policy is available as an ONNX model (160-dim input, 29-dim output) with baked-in observation normalization.</p>
<div class="deploy-options">
<a class="deploy-option" href="https://movin3d.com/" target="_blank" rel="noopener">
<div class="deploy-num">01</div>
<h3>MOVIN Studio</h3>
<p>Recording and processing software for MOVIN TRACIN markerless mocap. Captures full-body motion in real-time using LiDAR + vision at 60 FPS. Exports BVH with 51-joint humanoid skeleton, plus FBX for Blender, Maya, Unreal, and Unity.</p>
</a>
<a class="deploy-option" href="https://github.com/MOVIN3D/Motion-Player-ROS" target="_blank" rel="noopener">
<div class="deploy-num">02</div>
<h3>Motion-Player-ROS</h3>
<p>ROS 2 package for retargeting and previewing motion capture on the G1. Supports both playback of pre-recorded .pkl files and real-time retargeting from live MOVIN TRACIN data via OSC/UDP. Dual visualization shows the original human BVH skeleton alongside the retargeted robot motion in RViz.</p>
</a>
<a class="deploy-option" href="https://github.com/AIM-Intelligence/video2robot" target="_blank" rel="noopener">
<div class="deploy-num">03</div>
<h3>video2robot</h3>
<p>End-to-end pipeline converting any video to robot motion. Extracts 3D human pose via PromptHMR (SMPL-X), then retargets to the G1's 29-DOF joint space using GMR inverse kinematics. Works with YouTube, phone video, or AI-generated footage — no mocap hardware needed.</p>
</a>
<a class="deploy-option" href="https://github.com/mujocolab/mjlab" target="_blank" rel="noopener">
<div class="deploy-num">04</div>
<h3>mjlab</h3>
<p>GPU-accelerated RL training framework combining Isaac Lab's manager-based API with MuJoCo-Warp simulation. Trains PPO policies across 8,192 parallel environments on a single GPU. Motion imitation uses 14-body tracking with reward shaping for position, orientation, velocity, and collision avoidance.</p>
</a>
<a class="deploy-option" href="https://github.com/GDDG08/RoboJuDo" target="_blank" rel="noopener">
<div class="deploy-num">05</div>
<h3>RoboJuDo</h3>
<p>Plug-and-play sim2real deployment framework for humanoid robots. Modular architecture separates controller (joystick/keyboard/motion), environment (MuJoCo sim or real robot via Unitree SDK), and policy (ONNX/JIT). Supports seamless switching between sim2sim validation and real hardware with minimal code changes.</p>
</a>
<a class="deploy-option" href="https://github.com/zalo/mujoco_wasm" target="_blank" rel="noopener">
<div class="deploy-num">06</div>
<h3>MuJoCo WASM</h3>
<p>Browser-based 3D visualization of trained policies. Runs MuJoCo physics simulation via WebAssembly with ONNX Runtime Web for neural network inference — no install required. Each policy card in the gallery above is a live interactive viewer.</p>
</a>
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<p class="deploy-footer">Full observation vector spec and integration guide in the <a href="https://huggingface.co/datasets/exptech/g1-moves" target="_blank" rel="noopener">dataset README</a>.</p>
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<h2>EQUIPMENT</h2>
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<a class="equip-block" href="https://www.dell.com/en-us/shop/desktop-computers/dell-pro-max-tower-t2-desktop/spd/dell-pro-max-fct2250-desktop" target="_blank" rel="noopener">
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<h3>Training</h3>
<div class="equip-name">Dell Pro Max Tower T2</div>
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<span class="equipment-label">GPU</span>
<span class="equipment-value">RTX PRO 6000 (96 GB)</span>
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<span class="equipment-value">Core Ultra 9 285K</span>
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<span class="equipment-value">128 GB DDR5</span>
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<span class="equipment-value">8,192 parallel</span>
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<a class="equip-block" href="https://www.dell.com/en-us/shop/desktop-computers/dell-pro-max-with-gb10/spd/dell-pro-max-fcm1253-micro" target="_blank" rel="noopener">
<img class="equip-photo" src="dell_gb10.png" alt="Dell Pro Max with GB10">
<h3>Training</h3>
<div class="equip-name">Dell Pro Max with GB10</div>
<div class="equipment-grid">
<div class="equipment-item">
<span class="equipment-label">SoC</span>
<span class="equipment-value">GB10 Grace Blackwell</span>
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<span class="equipment-label">GPU</span>
<span class="equipment-value">Blackwell (1 PFLOP)</span>
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<span class="equipment-label">RAM</span>
<span class="equipment-value">128 GB unified</span>
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<span class="equipment-label">Envs</span>
<span class="equipment-value">1,024 parallel</span>
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<a class="equip-block" href="https://movin3d.com/" target="_blank" rel="noopener">
<img class="equip-photo" src="movin_tracin.webp" alt="MOVIN TRACIN">
<h3>Motion Capture</h3>
<div class="equip-name">MOVIN TRACIN</div>
<div class="equipment-grid">
<div class="equipment-item">
<span class="equipment-label">Capture</span>
<span class="equipment-value">MOVIN Studio</span>
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<span class="equipment-label">Sensing</span>
<span class="equipment-value">LiDAR + Vision</span>
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<span class="equipment-label">Retargeting</span>
<span class="equipment-value">Motion-Player-ROS</span>
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<span class="equipment-label">Output</span>
<span class="equipment-value">BVH (51 joints)</span>
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</a>
<a class="equip-block" href="https://www.unitree.com/g1/" target="_blank" rel="noopener">
<img class="equip-photo" src="unitree_g1.png" alt="Unitree G1">
<h3>Robot</h3>
<div class="equip-name">Unitree G1</div>
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<span class="equipment-label">Edition</span>
<span class="equipment-value">EDU</span>
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<span class="equipment-label">DOF</span>
<span class="equipment-value">29</span>
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<span class="equipment-label">Deploy</span>
<span class="equipment-value">RoboJuDo</span>
</div>
<div class="equipment-item">
<span class="equipment-label">Interface</span>
<span class="equipment-value">Ethernet</span>
</div>
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<a class="explore-hardware-btn" href="https://creatorfolio.co/mitchbookpro" target="_blank" rel="noopener">
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Explore Hardware
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</section>
<!-- Credits -->
<section class="credits reveal">
<h2>CREDITS</h2>
<div class="credits-grid">
<a class="credit-card" href="https://mitchchaiet.com/" target="_blank" rel="noopener">
<img class="credit-photo" src="credits/mitch.png" alt="Mitch Chaiet">
<div class="credit-role">Director + Development</div>
<div class="credit-name">Mitch Chaiet</div>
</a>
<a class="credit-card" href="https://www.linkedin.com/in/mollymaguire001/" target="_blank" rel="noopener">
<img class="credit-photo" src="credits/molly.jpg" alt="Molly Maguire">
<div class="credit-role">DIT</div>
<div class="credit-name">Molly Maguire</div>
</a>
<a class="credit-card" href="https://jasminecoro.com/" target="_blank" rel="noopener">
<img class="credit-photo" src="credits/jasmine.jpg" alt="Jasmine Coro">
<div class="credit-role">Dance</div>
<div class="credit-name">Jasmine Coro</div>
</a>
<a class="credit-card" href="https://www.backstage.com/u/mike-gassaway/" target="_blank" rel="noopener">
<img class="credit-photo" src="credits/mike.jpg" alt="Mike Gassaway">
<div class="credit-role">Karate</div>
<div class="credit-name">Mike Gassaway</div>
</a>
<a class="credit-card" href="https://mayacoro.com/" target="_blank" rel="noopener">
<img class="credit-photo" src="credits/maya.jpg" alt="Maya Coro">
<div class="credit-role">Fencing</div>
<div class="credit-name">Maya Coro</div>
</a>
<a class="credit-card" href="https://arcattack.com/about-us/crew/joe/" target="_blank" rel="noopener">
<img class="credit-photo" src="credits/joe.jpg" alt="Joe DiPrima">
<div class="credit-role">Deployment</div>
<div class="credit-name">Joe DiPrima</div>
</a>
<a class="credit-card" href="https://arcattack.com/about-us/crew/john/" target="_blank" rel="noopener">
<img class="credit-photo" src="credits/john.png" alt="John DiPrima">
<div class="credit-role">Deployment</div>
<div class="credit-name">John DiPrima</div>
</a>
</div>
</section>
<!-- Citation -->
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<h2>CITATION</h2>
<p class="citation-intro">If you use G1 Moves in your research or project, please cite:</p>
<a class="whitepaper-btn" href="G1_Moves_Whitepaper.pdf" target="_blank" rel="noopener">
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<pre><code>@misc{chaiet2026g1moves,
title = {G1 Moves: A Motion Capture Dataset for Unitree G1 Humanoid Robot},
author = {Chaiet, Mitch},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/exptech/g1-moves},
note = {60 motion capture clips with trained RL policies for sim-to-real transfer}
}</code></pre>
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</section>
<!-- Disclaimer -->
<section class="disclaimer reveal">
<div class="disclaimer-inner">
<h3>Safety Disclaimer &amp; Limitation of Liability</h3>
<p><strong>WARNING: Deploying learned locomotion policies to physical robots is inherently dangerous and can cause serious injury, death, or property damage.</strong> The policies provided in this dataset are trained in simulation and have not been validated for safe real-world operation. Sim-to-real transfer involves unpredictable failure modes including but not limited to: sudden loss of balance, uncontrolled high-speed limb movements, unexpected falls, collisions with people or objects, and hardware damage.</p>
<p><strong>Do not deploy these policies to a physical robot unless you have:</strong> (1) advanced expertise in sim-to-real transfer, robot safety systems, and real-time control; (2) appropriate physical safety infrastructure including overhead tethers, safety enclosures, and emergency stop mechanisms; (3) completed thorough sim-to-sim validation; (4) conducted a formal risk assessment for your specific deployment environment; and (5) ensured all personnel in the vicinity are trained on emergency procedures.</p>
<p><strong>LIMITATION OF LIABILITY:</strong> THE POLICIES, MODELS, DATA, AND SOFTWARE IN THIS DATASET ARE PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, SAFETY, OR NONINFRINGEMENT. IN NO EVENT SHALL EXPERIENTIAL TECHNOLOGIES, ITS AFFILIATES, CONTRIBUTORS, OR LICENSORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, CONSEQUENTIAL, OR PUNITIVE DAMAGES (INCLUDING BUT NOT LIMITED TO PERSONAL INJURY, DEATH, PROPERTY DAMAGE, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES, LOSS OF USE, DATA, OR PROFITS, OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS DATASET OR ITS DERIVATIVES, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.</p>
<p>By downloading, using, or deploying any part of this dataset, you acknowledge that you assume all risk and full responsibility for any consequences arising from its use. You agree to indemnify and hold harmless Experiential Technologies, its officers, directors, employees, and agents from any claims, damages, or liabilities arising from your use of these materials.</p>
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