Upload README.md with huggingface_hub

README.md

@@ -1,3 +1,114 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-nc-4.0
+tags:
+- robotics
+- world-model
+- jepa
+- planning
+- pytorch
+library_name: pytorch
+pipeline_tag: robotics
+datasets:
+- facebook/jepa-wms
+---
+
+# JEPA-WMs: Pretrained World Models
+
+This repository contains pretrained world model checkpoints from the paper
+["What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?"](https://arxiv.org/abs/2512.24497)
+
+## Available Models
+
+### JEPA-WM Models
+
+| Model | Environment | Resolution | Encoder | Pred. Depth |
+|-------|-------------|------------|---------|-------------|
+| `jepa_wm_droid` | DROID & RoboCasa | 256×256 | DINOv3 ViT-L/16 | 12 |
+| `jepa_wm_metaworld` | Metaworld | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `jepa_wm_pusht` | Push-T | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `jepa_wm_pointmaze` | PointMaze | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `jepa_wm_wall` | Wall | 224×224 | DINOv2 ViT-S/14 | 6 |
+
+### DINO-WM Baseline Models
+
+| Model | Environment | Resolution | Encoder | Pred. Depth |
+|-------|-------------|------------|---------|-------------|
+| `dino_wm_droid` | DROID & RoboCasa | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `dino_wm_metaworld` | Metaworld | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `dino_wm_pusht` | Push-T | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `dino_wm_pointmaze` | PointMaze | 224×224 | DINOv2 ViT-S/14 | 6 |
+| `dino_wm_wall` | Wall | 224×224 | DINOv2 ViT-S/14 | 6 |
+
+### V-JEPA-2-AC Baseline Models
+
+| Model | Environment | Resolution | Encoder | Pred. Depth |
+|-------|-------------|------------|---------|-------------|
+| `vjepa2_ac_droid` | DROID & RoboCasa | 256×256 | V-JEPA-2 ViT-G/16 | 24 |
+| `vjepa2_ac_oss` | DROID & RoboCasa | 256×256 | V-JEPA-2 ViT-G/16 | 24 |
+
+### VM2M Decoder Heads
+
+| Model | Encoder | Resolution |
+|-------|---------|------------|
+| `dinov2_vits_224` | DINOv2 ViT-S/14 | 224×224 |
+| `dinov2_vits_224_INet` | DINOv2 ViT-S/14 | 224×224 |
+| `dinov3_vitl_256_INet` | DINOv3 ViT-L/16 | 256×256 |
+| `vjepa2_vitg_256_INet` | V-JEPA-2 ViT-G/16 | 256×256 |
+
+## Usage
+
+### Via PyTorch Hub (Recommended)
+
+```python
+import torch
+
+# Load JEPA-WM models
+model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'jepa_wm_droid')
+model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'jepa_wm_metaworld')
+
+# Load DINO-WM baselines
+model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'dino_wm_metaworld')
+
+# Load V-JEPA-2-AC baseline
+model, preprocessor = torch.hub.load('facebookresearch/jepa-wms', 'vjepa2_ac_droid')
+```
+
+### Via Hugging Face Hub
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download a specific checkpoint
+checkpoint_path = hf_hub_download(
+    repo_id="facebook/jepa-wms",
+    filename="jepa_wm_droid.pth.tar"
+)
+
+# Load with PyTorch
+checkpoint = torch.load(checkpoint_path, map_location="cpu")
+```
+
+## Citation
+
+```bibtex
+@misc{terver2025drivessuccessphysicalplanning,
+      title={What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?},
+      author={Basile Terver and Tsung-Yen Yang and Jean Ponce and Adrien Bardes and Yann LeCun},
+      year={2025},
+      eprint={2512.24497},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2512.24497},
+}
+```
+
+## License
+
+These models are licensed under [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
+
+## Links
+
+- 📄 [Paper](https://arxiv.org/abs/2512.24497)
+- 💻 [GitHub Repository](https://github.com/facebookresearch/jepa-wms)
+- 🤗 [Dataset](https://huggingface.co/datasets/facebook/jepa-wms)