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README.md

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----
-license: mit
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+---
+language: en
+tags: [robotics, 6-axis-arm, visual-policy, pytorch, imitation-learning]
+license: apache-2.0
+---
+
+# 6Net — 6-Axis Visual Robot Policy (~115M)
+
+Custom transformer policy for visual 6-DoF robot arm control. Trained from scratch (no LoRA).
+
+| Component | Detail | Params |
+|---|---|---|
+| Visual Encoder | ResNet-18 fine-tuned | ~11.7M |
+| Visual Projection | Linear(512→768) | ~0.4M |
+| State Encoder | MLP(6→256→768) | ~0.2M |
+| Transformer | 14L · d=768 · 12h · ffn=3072 | ~99.1M |
+| Action Head | MLP(768→256→6) | ~0.2M |
+| **Total** | | **~111M** |
+
+**Dataset:** `synthetic` · **Steps:** 455 · **Eff. batch:** 32
+
+## Inference
+```python
+import torch
+from train_6net_local import SixNet, Config
+import torchvision.transforms as T
+from PIL import Image
+
+model = SixNet(Config())
+ckpt  = torch.load("6net_final.pt", map_location="cpu")
+model.load_state_dict(ckpt["model_state"])
+model.eval()
+
+tf  = T.Compose([T.Resize((224,224)), T.ToTensor(),
+                 T.Normalize([.485,.456,.406],[.229,.224,.225])])
+img = tf(Image.open("cam.jpg")).unsqueeze(0)   # (1,3,224,224)
+jts = torch.zeros(1, 6)                         # current joint angles (rad)
+with torch.no_grad():
+    action = model(img, jts)                     # (1,6) predicted targets
+```