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README.md

@@ -1,3 +1,83 @@
 ---
 license: mit
+tags:
+  - robotics
+  - imitation-learning
+  - diffusion-policy
+  - robomimic
+  - manipulation
 ---
+
+# ReGuide — Checkpoints
+
+Pretrained **diffusion policy** and **visual dynamics model** checkpoints for
+**ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies**
+(Lin, Shakkottai, Kalathil, Kumar — Texas A&M University).
+
+ReGuide is a self-improving framework that recycles *guided rollouts* as on-policy
+recovery data to fix covariate shift in behavior-cloned diffusion policies, improving
+base-policy success by **1.3–7.7×** on Robomimic. See the
+[project page](https://reguide-project.github.io) and paper for method details.
+
+Checkpoints are organized by benchmark:
+
+- [`robomimic/`](robomimic) — Can, Square, Transport, Tool Hang (available now)
+- `libero/` — coming soon
+
+## Robomimic tasks
+
+| Task        | Demos | Notes |
+|-------------|:-----:|-------|
+| `can`       | 15    | policy + dynamics model |
+| `square`    | 30    | policy + dynamics model |
+| `transport` | 10    | policy + dynamics model |
+| `tool_hang` | 80    | policy + dynamics model |
+
+## Repository layout
+
+```
+robomimic/diffusion_policy/<task>/<variant>/
+├── checkpoints/<epoch>.ckpt     # policy weights (~4.3–5.3 GB)
+├── normalizer.pth               # observation/action normalizer
+└── .hydra/                       # full training config
+
+robomimic/dyn_model/<task>/
+├── checkpoints/model_<epoch>.pth # visual dynamics model (~0.5–1.5 GB)
+├── normalizer.pth
+└── hydra.yaml                    # training config
+```
+
+The number in each checkpoint filename (e.g. `880.ckpt`) is the training epoch.
+
+### Diffusion policy variants
+
+| Variant                 | What it is |
+|-------------------------|------------|
+| `base_policy`           | Diffusion policy trained on the original demonstrations only. |
+| `ReGuide-FS`            | Retrained from scratch on demos + guided rollouts. |
+| `ReGuide-FT-iteration1` | Base checkpoint fine-tuned on demos + guided rollouts (first iteration). |
+| `ReGuide-FT-iteration2` | Second fine-tuning iteration on freshly collected guided rollouts. |
+| `ReGuide-FS-FT`         | ReGuide-FT applied on top of a ReGuide-FS policy (best on Can/Square/Transport). |
+
+Each checkpoint ships with its full Hydra training config (`.hydra/` for policies,
+`hydra.yaml` for dynamics models), which records the exact hyperparameters used. Paths
+inside the configs are relative.
+
+## Code
+
+Training and evaluation code: **TODO: add GitHub repo link**
+
+## Citation
+
+```bibtex
+@article{lin2026reguide,
+  title   = {ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies},
+  author  = {Lin, Tzu-Hsiang and Shakkottai, Srinivas and Kalathil, Dileep and Kumar, P. R.},
+  year    = {2026},
+  url     = {https://reguide-project.github.io}
+}
+```
+
+## License
+
+Released under the [MIT License](https://opensource.org/license/mit).

robomimic/README.md

@@ -1,79 +0,0 @@
----
-license: mit
-tags:
-  - robotics
-  - imitation-learning
-  - diffusion-policy
-  - robomimic
-  - manipulation
----
-
-# ReGuide — Robomimic Checkpoints
-
-Pretrained **diffusion policy** and **visual dynamics model** checkpoints for
-**ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies**
-(Lin, Shakkottai, Kalathil, Kumar — Texas A&M University).
-
-ReGuide is a self-improving framework that recycles *guided rollouts* as on-policy
-recovery data to fix covariate shift in behavior-cloned diffusion policies, improving
-base-policy success by **1.3–7.7×** on Robomimic. See the
-[project page](https://reguide-project.github.io) and paper for method details.
-
-## Tasks
-
-| Task        | Demos | Notes |
-|-------------|:-----:|-------|
-| `can`       | 15    | policy + dynamics model |
-| `square`    | 30    | policy + dynamics model |
-| `transport` | 10    | policy + dynamics model |
-| `tool_hang` | 80    | policy + dynamics model |
-
-
-## Repository layout
-
-```
-diffusion_policy/<task>/<variant>/
-├── checkpoints/<epoch>.ckpt     # policy weights (~4.3–5.3 GB)
-├── normalizer.pth               # observation/action normalizer
-└── .hydra/                       # full training config
-
-dyn_model/<task>/
-├── checkpoints/model_<epoch>.pth # visual dynamics model (~0.5–1.5 GB)
-├── normalizer.pth
-└── hydra.yaml                    # training config
-```
-
-The number in each checkpoint filename (e.g. `880.ckpt`) is the training epoch.
-
-### Diffusion policy variants
-
-| Variant                 | What it is |
-|-------------------------|------------|
-| `base_policy`           | Diffusion policy trained on the original demonstrations only. |
-| `ReGuide-FS`            | Retrained from scratch on demos + guided rollouts. |
-| `ReGuide-FT-iteration1` | Base checkpoint fine-tuned on demos + guided rollouts (first iteration). |
-| `ReGuide-FT-iteration2` | Second fine-tuning iteration on freshly collected guided rollouts. |
-| `ReGuide-FS-FT`         | ReGuide-FT applied on top of a ReGuide-FS policy (best on Can/Square/Transport). |
-
-Each checkpoint ships with its full Hydra training config (`.hydra/` for policies,
-`hydra.yaml` for dynamics models), which records the exact hyperparameters used. Paths
-inside the configs are relative.
-
-## Code
-
-Training and evaluation code: **TODO: add GitHub repo link**
-
-## Citation
-
-```bibtex
-@article{lin2026reguide,
-  title   = {ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies},
-  author  = {Lin, Tzu-Hsiang and Shakkottai, Srinivas and Kalathil, Dileep and Kumar, P. R.},
-  year    = {2026},
-  url     = {https://reguide-project.github.io}
-}
-```
-
-## License
-
-Released under the [MIT License](https://opensource.org/license/mit).