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README.md

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+---
+license: mit
+tags:
+- robomimic
+- diffusion-policy
+- imitation-learning
+- robosuite
+---
+
+# Diffusion Policy — NutAssemblySquare (Square/PH/low-dim)
+
+A robomimic `DiffusionPolicyUNet` checkpoint (UNet + DDPM noise scheduler,
+action-chunked receding-horizon control: `observation_horizon=2`,
+`action_horizon=8`, `prediction_horizon=16`) trained for 2000 epochs on
+robomimic's public 200-demo Square/PH/low-dim dataset.
+
+Rollout success rate (20 episodes, evaluated every 200 epochs):
+
+| Epoch | 200 | 400 | 600 | 800 | 1000 | 1200 | 1400 | 1600 | **1800** | 2000 |
+|---|---|---|---|---|---|---|---|---|---|---|
+| Success | 85% | 85% | 85% | 70% | 75% | 90% | 85% | 85% | **95%** | 90% |
+
+`checkpoints/model_epoch_1800_low_dim_success_0.95.pth` (95% success, first
+epoch to hit the run's peak) is the checkpoint used as the frozen base
+policy for downstream residual-RL experiments.
+
+This is a second, parallel base-policy lineage alongside a BC-RNN baseline
+trained on the same task/dataset, for a residual-RL project comparing how a
+heuristic-triggered SAC residual correction interacts with each. Full
+writeup, code, and results: https://github.com (see the project's own
+README for the actual repo link — not filled in here since it isn't public).
+
+Downloading a specific checkpoint:
+```python
+from huggingface_hub import hf_hub_download
+hf_hub_download(
+    "georginio2000/diffusion-square-nutassembly",
+    "checkpoints/model_epoch_1800_low_dim_success_0.95.pth",
+)
+```