---
library_name: lerobot
license: apache-2.0
pipeline_tag: robotics
base_model: amd/CAE-FastWAM
datasets:
- yuanty/LIBERO-fastwam
tags:
- cae_fastwam
- lerobot
- libero
- robotics
- world-action-model
---

# CAE-FastWAM (LeRobot format)

[CAE-FastWAM](https://huggingface.co/amd/CAE-FastWAM) converted to the [LeRobot](https://github.com/huggingface/lerobot) checkpoint format. CAE-FastWAM is a [FastWAM](https://arxiv.org/abs/2603.16666) world action model with a Compact Action Expert: a small (32-wide) action expert denoises latent action tokens, and a LIBERO-suite-conditioned action decoder turns them into the action chunk, on top of FastWAM's Wan2.2-TI2V-5B video expert.

The weights are the same as [amd/CAE-FastWAM](https://huggingface.co/amd/CAE-FastWAM); this repo only changes the layout (`config.json`, `model.safetensors`, and the LeRobot pre/post-processors with the per-suite normalisation statistics). It also carries the frozen action-VAE encoder, so the policy can be fine-tuned with the original CAE-FastWAM objective.

> **Note:** loading this checkpoint needs the `cae_fastwam` policy type, which is not in a LeRobot release yet. This card will be updated with install instructions once it is available in LeRobot.

## Inputs and outputs

| Feature | Type | Shape |
| --- | --- | --- |
| `observation.images.image` (agent view) | visual | (3, 224, 224) |
| `observation.images.image2` (wrist view) | visual | (3, 224, 224) |
| `observation.state` | state | (8,) |
| `action` | action | (7,) |

Select the LIBERO suite with `--policy.task_suite` (`libero_spatial`, `libero_object`, `libero_goal` or `libero_10`); evaluate one suite per run.

## Results

LIBERO, bfloat16, 10 denoising steps, 50 episodes per task:

| Suite | Success rate | Episodes |
| --- | ---: | ---: |
| libero_spatial | 97.0% | 500 |
| libero_object | 99.8% | 500 |
| libero_goal | 94.4% | 500 |
| libero_10 | 95.6% | 500 |
| **Average** | **96.7%** | 2000 |

Reproduce (per suite; `--env.episode_length=700` for `libero_10`), on one AMD Instinct MI355X:

```bash
lerobot-eval --policy.path=amd/CAE-FastWAM-lerobot --policy.task_suite=libero_spatial \
  --env.type=libero --env.task=libero_spatial \
  --env.observation_height=256 --env.observation_width=256 --env.episode_length=400 \
  --eval.batch_size=5 --eval.n_episodes=50 --seed=42
```

LIBERO stack: the one pinned by LeRobot's LIBERO benchmark image (hf-libero 0.1.4, robosuite 1.4.0, MuJoCo 3.3.1), CPU physics, OSMesa rendering. The MuJoCo version matters: with MuJoCo 3.8.1 the `libero_spatial` task 5 scene settles differently and the same evaluation scored 94.5% on average. Please report the MuJoCo version with LIBERO results.

For reference, the [amd/CAE-FastWAM](https://huggingface.co/amd/CAE-FastWAM) card reports 97.28 ± 0.20% with GEAK-optimized inference and a GPU-simulated LIBERO (mean of 11 runs of 2000 episodes).

## Fine-tuning

The checkpoint holds the frozen action-VAE encoder and decoder, so `lerobot-train --policy.path=amd/CAE-FastWAM-lerobot --policy.task_suite=<suite> --accelerator.mixed_precision=bf16 ...` fine-tunes the video and action experts with the original CAE-FastWAM objective, one suite per run.

## Provenance and license

Converted from [amd/CAE-FastWAM](https://huggingface.co/amd/CAE-FastWAM) (Apache-2.0, Advanced Micro Devices, Inc.), a fine-tune of [Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) with the [FastWAM](https://github.com/yuantianyuan01/FastWAM) recipe on LIBERO. See `LICENSE` and `NOTICE`.