Remove code snippets from pipeline, make cards clickable links, GB10 envs to 1024

CLAUDE.md

@@ -7,6 +7,7 @@
 
 | ID | Time | T | Title | Read |
 |----|------|---|-------|------|
+| #2374 | 6:52 PM | 🔄 | Pipeline Tool Cards Refactored as Clickable Links | ~316 |
 | #2364 | 6:25 PM | ✅ | Dell Pro Max GB10 Equipment Specs Updated | ~254 |
 | #2361 | 6:17 PM | ✅ | Updated team credits with role reorganization and new contributors | ~250 |
 | #2360 | " | ✅ | Updated credits to reflect development role | ~190 |

index.html

@@ -126,65 +126,36 @@
       <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">
-        <div class="deploy-option">
+        <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>
-          <p><a href="https://movin3d.com/" target="_blank" rel="noopener">movin3d.com</a></p>
-        </div>
-        <div class="deploy-option">
+        </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 <code>.pkl</code> 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>
-          <p><a href="https://github.com/MOVIN3D/Motion-Player-ROS" target="_blank" rel="noopener">github.com/MOVIN3D/Motion-Player-ROS</a></p>
-          <pre><code># Playback mode
-ros2 launch motion_player player.launch.py \
-  motion_file:=clip.pkl bvh_file:=clip.bvh
-
-# Real-time mocap
-ros2 launch motion_player realtime.launch.py \
-  port:=9000 human_height:=1.75</code></pre>
-        </div>
-        <div class="deploy-option">
+          <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 <a href="https://github.com/AIM-Intelligence/video2robot" target="_blank" rel="noopener">PromptHMR</a> (SMPL-X), then retargets to the G1's 29-DOF joint space using <a href="https://github.com/YanjieZe/GMR" target="_blank" rel="noopener">GMR</a> inverse kinematics. Works with YouTube, phone video, or AI-generated footage — no mocap hardware needed.</p>
-          <p><a href="https://github.com/AIM-Intelligence/video2robot" target="_blank" rel="noopener">github.com/AIM-Intelligence/video2robot</a></p>
-          <pre><code># Extract pose from video
-python scripts/extract_pose.py --project data/clip
-
-# Retarget to G1
-python scripts/convert_to_robot.py \
-  --project data/clip --robot unitree_g1</code></pre>
-        </div>
-        <div class="deploy-option">
+          <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 <a href="https://github.com/google-deepmind/mujoco_warp" target="_blank" rel="noopener">MuJoCo-Warp</a> 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>
-          <p><a href="https://github.com/mujocolab/mjlab" target="_blank" rel="noopener">github.com/mujocolab/mjlab</a></p>
-          <pre><code># Train a motion imitation policy
-uv run train Mjlab-Tracking-Flat-Unitree-G1 \
-  --env.commands.motion.motion-file clip.npz \
-  --env.scene.num-envs 8192 \
-  --agent.max-iterations 30000</code></pre>
-        </div>
-        <div class="deploy-option">
+          <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>
-          <p><a href="https://github.com/GDDG08/RoboJuDo" target="_blank" rel="noopener">github.com/GDDG08/RoboJuDo</a></p>
-          <pre><code># Sim2sim validation
-python scripts/run_pipeline.py --config=g1
-
-# Deploy to physical G1
-python scripts/run_pipeline.py --config=g1_real</code></pre>
-        </div>
-        <div class="deploy-option">
+        </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>
-          <p><a href="https://github.com/zalo/mujoco_wasm" target="_blank" rel="noopener">github.com/zalo/mujoco_wasm</a></p>
-        </div>
+        </a>
       </div>
       <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>
     </div>
@@ -269,7 +240,7 @@ python scripts/run_pipeline.py --config=g1_real</code></pre>
           </div>
           <div class="equipment-item">
             <span class="equipment-label">Envs</span>
-            <span class="equipment-value">8,192 parallel</span>
+            <span class="equipment-value">1,024 parallel</span>
           </div>
         </div>
       </div>

style.css

@@ -815,16 +815,20 @@ a:hover { opacity: 0.8; }
   gap: 1.5rem;
 }
 
-.deploy-option {
+a.deploy-option, .deploy-option {
   background: var(--bg-card);
   border: 1px solid var(--border);
   border-radius: var(--radius);
   padding: 1.5rem;
   transition: border-color 0.2s;
+  text-decoration: none;
+  color: inherit;
+  display: block;
+  cursor: pointer;
 }
 
-.deploy-option:hover {
-  border-color: var(--border-hover);
+a.deploy-option:hover, .deploy-option:hover {
+  border-color: var(--accent);
 }
 
 .deploy-num {
@@ -852,26 +856,6 @@ a:hover { opacity: 0.8; }
   margin-bottom: 0.75rem;
 }
 
-.deploy-option p a {
-  color: var(--accent);
-  text-decoration: none;
-}
-
-.deploy-option pre {
-  background: var(--bg-primary);
-  border: 1px solid var(--border);
-  border-radius: 6px;
-  padding: 0.75rem;
-  overflow-x: auto;
-  margin: 0;
-}
-
-.deploy-option code {
-  font-family: var(--font-mono);
-  font-size: 0.7rem;
-  color: var(--text-primary);
-  line-height: 1.5;
-}
 
 .deploy-footer {
   color: var(--text-tertiary);