| --- |
| library_name: pytorch |
| pipeline_tag: robotics |
| tags: |
| - robotics |
| - world-action-model |
| - manipulation |
| --- |
| |
| # PrismWAM |
|
|
| Checkpoints for **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) |
|
|
| | Folder | Benchmark | Actions | Result | |
| | --- | --- | --- | ---: | |
| | `robotwin/` | RoboTwin 2.0, Clean → Randomized | 14-D absolute joints | 70.57 | |
| | `robodojo/` | RoboDojo | 14-D absolute joints | 25.10 / 18.55 | |
| | `libero/` | LIBERO, 4 suites | 7-D delta end effector + gripper | 99.35 | |
| | `init/` | training initializers: depth VAEs, RGB VAE, action-expert init | | | |
|
|
| Success rates in %; RoboDojo is score / success rate. Each model folder holds `config.yaml`, |
| `policy.pt`, the RGB and depth VAEs, and its action statistics. |
|
|
| ## Usage |
|
|
| Install the [code](https://github.com/THUSI-Lab/PrismWAM), then from the repository root: |
|
|
| ```bash |
| hf download THUSI-Lab/PrismWAM --include "robotwin/*" --local-dir weights |
| ``` |
|
|
| ```python |
| from prismwam import load_pretrained |
| |
| policy = load_pretrained("robotwin") # also fetches the T5 text encoder on first use |
| observation["prompt"] = policy.format_prompt("beat the block with the hammer") |
| out = policy.predict(observation, seed=0) # out["action"]: 32 x 14 |
| ``` |
|
|
| The observation format, benchmark evaluation and training recipes are documented in the code |
| repository. |
|
|
| ## 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} |
| } |
| ``` |
|
|
