---
library_name: pytorch
pipeline_tag: robotics
tags:
- robotics
- world-action-model
- manipulation
---

# PrismWAM\*

The PrismWAM\* checkpoint for RoboTwin 2.0, from **PrismWAM: Decoupling State Representations in
World Action Models**.

[Code](https://github.com/THUSI-Lab/PrismWAM) · [Project page](https://prism-wam.github.io/) · [arXiv](https://arxiv.org/abs/2610.26564) · [PrismWAM checkpoints](https://huggingface.co/THUSI-Lab/PrismWAM)

| Folder | Benchmark | Actions | Result |
| --- | --- | --- | ---: |
| `robotwin/` | RoboTwin 2.0, Clean → Randomized | 14-D absolute joints | 83.67 |

Average success rate in % over the 50 tasks in clean and randomized scenes, 100 episodes per task;
trained on clean scenes only.

The model folder is laid out like those of [THUSI-Lab/PrismWAM](https://huggingface.co/THUSI-Lab/PrismWAM):
`config.yaml`, `policy.pt`, the RGB and depth VAEs, and the action statistics. On first use the
code also fetches the T5 text encoder of
[Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) and the CLIP image encoder of
[Wan2.1-I2V-14B-480P](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P).

## Usage

Install the [code](https://github.com/THUSI-Lab/PrismWAM), then from the repository root:

```bash
hf download THUSI-Lab/PrismWAM-Star --include "robotwin/*" --local-dir weights_star
```

```python
from prismwam import load_pretrained

policy = load_pretrained("prismwam_star/robotwin")   # uses weights_star/robotwin
observation["prompt"] = policy.format_prompt("lift the bottle with the right arm")
out = policy.predict(observation, seed=0)        # out["action"]: 32 x 14
```

The observation format and the RoboTwin evaluation are documented in the
[code repository](https://github.com/THUSI-Lab/PrismWAM/blob/main/docs/benchmarks/robotwin.md#evaluate).

## Citation

```bibtex
@misc{prism,
  title         = {PrismWAM: Decoupling State Representations in World Action Models},
  author        = {Xin, Tianyu and Zhong, Wanqi and Hu, Bowen and Guo, Zhangchi and Wang, Lijie and Wang, Yiming and Liao, Haoran and Yu, Heyang and Tian, Xiaoyu and Yu, Zhiding and Li, Yiming},
  year          = {2026},
  eprint        = {2610.26564},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2610.26564}
}
```