---
license: apache-2.0
tags:
  - robotics
  - world-model
  - world-action-model
  - robotwin
  - manipulation
  - flow-matching
library_name: instinct-wam
---

# InstinctWAM (RoboTwin 2.0)

A compact **World-Action Model** (video world-model + action expert) for bimanual
manipulation on RoboTwin 2.0 (aloha-agilex). Trained **from scratch** using only
permissively-licensed inputs:

- **WAN-2.2-TI2V-5B** (teacher / structured-slice init) — Apache-2.0
- **RoboTwin 2.0** trajectories — Apache-2.0
- No third-party trained checkpoints were used (see `NOTICE`).

## Architecture
- Compact video DiT (dim 2048, 12 layers, 16 heads, head_dim 128; ~849M) distilled from WAN-2.2 via
  structured layer slicing; future-latent size 192×160 (RT variant).
- Action expert (dim 768, 12 layers, chunk 16, state/action dim 14; ~139M), joint attention (MoT).
- Flow-matching / rectified-flow objective; asymmetric denoising at inference (RT: 2 video-refresh steps).

## Training
Curated 16 hard/long-horizon RoboTwin tasks → 9,254 episodes → 2.55M samples.
Three-stage recipe, **80k steps/stage** on 2×H100:
1. Compact video distillation (hidden+motion KD in 256-d PCA space + GT flow-matching)
2. Frozen-video action training
3. Joint video-action refinement

## Evaluation (16 tasks × 20 episodes, success rate %)
| budget | demo_clean | demo_randomized |
|---|---|---|
| 80k×3 (this model) | **61.6** | **58.4** |

## Files
- `instinctwam_robotwin2.pt` — the deployable weights (final, joint-refined).
- `action_stats.json` — qpos mean/std normalization (required at inference).
- `NOTICE` — third-party attribution (WAN-2.2, RoboTwin 2.0).

## Usage
Code / architecture: **https://github.com/william-Dic/instinctwam_clean**

```python
from instinct_wam import InstinctWAM
model = InstinctWAM.from_pretrained("GM717/instinct-wam-robotwin-clean")  # pulls instinctwam_robotwin2.pt
```
Pair with `action_stats.json` for action de-normalization. RT inference settings:
`num_inference_steps 10`, `video_refresh_steps [0, 1]`, `future_video_size [192, 160]`.

License: Apache-2.0 (see `NOTICE` for third-party components).