---
language:
- en
library_name: pytorch
tags:
- robotics
- robot-learning
- imitation-learning
- embodied-ai
- world-model
- vision-language-action
- libero
- robotwin
---

# Enfold Checkpoints

This repository provides the official checkpoints for **Enfold: Folding World-Generator Computation into Predictive Representations for Efficient Embodied Control**

## What is included

Each benchmark has one policy checkpoint and one matching normalization-statistics file. Always use a checkpoint with the statistics file from the same row.

| Benchmark | Policy checkpoint | Normalization statistics | Evaluation setup |
| --- | --- | --- | --- |
| LIBERO | `enfold_libero.pt` | `libero_dataset_stats.pt` | Two camera views, concatenated to 224 × 448; 7-D actions and 8-D proprioception |
| RoboTwin | `enfold_robotwin.pt` | `robotwin_dataset_stats.pt` | Three camera views at 384 × 320; 14-D actions and proprioception |

## Download

Install the Hub client, then download only the files for the benchmark you plan to evaluate:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="richardxyt/Enfold",
    local_dir="checkpoints/enfold_libero",
    allow_patterns=["enfold_libero.pt", "libero_dataset_stats.pt"],
)
```

For RoboTwin, replace the two filenames with `enfold_robotwin.pt` and `robotwin_dataset_stats.pt`.

## Evaluation

First install the Enfold source code and its dependencies, then configure the required external model assets (Cosmos-Predict2.5, Cosmos tokenizer/VAE and text-encoder assets, DINOv3 ViT-H/16+). The source configuration contains paths that must be changed for your machine.

From the Enfold project root, evaluate the matching checkpoint/statistics pair:

```bash
# LIBERO
bash eval.sh libero \
  checkpoints/enfold_libero/enfold_libero.pt \
  checkpoints/enfold_libero/libero_dataset_stats.pt

# RoboTwin
bash eval.sh robotwin \
  checkpoints/enfold_robotwin/enfold_robotwin.pt \
  checkpoints/enfold_robotwin/robotwin_dataset_stats.pt
```

See the Enfold source repository for environment setup, dataset preparation, optional Hydra overrides, multi-GPU evaluation.

## Citation

If these checkpoints help your research, please cite the model repository:

```bibtex
@software{enfold2026,
  title  = {Enfold: Robotics Policy Checkpoints},
  author = {Enfold Authors},
  year   = {2026},
  url    = {https://huggingface.co/richardxyt/Enfold}
}
```