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license: apache-2.0
base_model: Wan-AI/Wan2.2-TI2V-5B
pipeline_tag: robotics
tags: [robotics, world-model, video-prediction, fastwam, wan2.2, robodojo]
---
# FastWAM — RoboDojo Precision suite, vertical 576x256
FastWAM (Wan2.2-TI2V-5B backbone) trained on the RoboDojo **Precision** suite, 8 tasks.
The full 100,000-step run, plus the 80k checkpoint kept for comparison.
**Use `step_100000.pt`.** The cosine schedule anneals hard at the end: val_loss went
0.1011 at 80k to **0.0272** at 100k. The 80k file was uploaded while the run was still in
flight and is kept only so the difference is checkable, not because it is a useful
alternative.
## Training setup
| | |
|---|---|
| base model | `Wan-AI/Wan2.2-TI2V-5B` |
| ActionDiT backbone | linear-interpolated from the Wan2.2 DiT, alpha-scaled, 1024 hidden |
| steps | 100,000 |
| GPUs | 8x H100 80GB, DeepSpeed ZeRO-1 |
| batch | 8 per GPU = **global batch 64** |
| lr | 1e-4 cosine, weight decay 1e-2 |
| grad accumulation | 1 |
| val_loss | **0.0272** at 100k (0.1011 at 80k) |
## Canvas and cameras
The **vertical** 3-camera stack, not the T ("robotwin") composite:
```
576x256 canvas = three 192x256 tiles stacked top to bottom
cam_high
cam_left_wrist
cam_right_wrist
```
Each tile is a uniform 0.8x of the 240x320 source, so the 4:3 aspect is preserved exactly
and the canvas equals the tile sum — no crop. The alternative T layout on this dataset
squeezes every tile to 0.8:1; this arm exists to avoid that.
```
num_frames 33, action_video_freq_ratio 4 -> 9 video frames + 32 action steps
at 25 fps that is 1.28 s of future action per window
```
## Data
RoboDojo Precision suite, 8 tasks x 100 episodes = 800 episodes / 368,459 frames at 25 fps,
3 cameras at 320x240 H.264, 14-D action and state (left arm 0:6, left gripper 6:7,
right arm 7:13, right gripper 13:14).
Built from `RoboDojo-Benchmark/RoboDojo` by pulling the 8 Precision-dimension blocks and
re-indexing to a standalone dataset. Tasks: build_tower, deposit_coin, fasten_screws,
insert_key, insert_tubes, play_Xylophone, plug_in_charger, pour_balls_into_vase.
## What is in here
```
step_100000.pt final weights (12 GB) -- use this one
step_080000.pt intermediate, kept for comparison
dataset_stats.json z-score normalization computed over the 800-episode subset
config.yaml the resolved training config for this run
```
**`dataset_stats.json` is required at inference.** The normalization is z-score computed
from this dataset; feeding un-normalized actions, or stats from a different corpus, gives
garbage. The 80 GB `state/` directory (optimizer moments, ZeRO shards, RNG) is not
included — this checkpoint is for inference, not for resuming.
## Notes
- Trained with `mot_checkpoint_mixed_attn: false` and the uncond (no action conditioning
on the video DiT) recipe.
- The run was interrupted once at step ~58.9k and resumed from the 55k **state**
checkpoint — optimizer moments, LR schedule position and step counter all restored, so
the cosine schedule is intact. Resuming from weights alone would have restarted the
schedule and is not what happened here.
|