---
license: other
language:
- en
tags:
- robotics
- robotwin
- imitation-learning
- video-generation
- action-generation
- vam
- wan
- openwam
- eef
- mask-v3
pipeline_tag: video-to-video
base_model: Wan-AI/Wan2.2-TI2V-5B
library_name: pytorch
---

# RoboTwin ICL v3 ARX-X5 VAM TI2V-5B OpenWAM EEF Mask-v3 Refdrop10 Train20 100K

This repository contains a 100k-step VAM checkpoint trained on the RoboTwin ICL-paired v3 dataset for the ARX-X5 target robot.

This is the **EEF reference-video** mask-v3 variant: during training the model used cross-embodiment full reference videos with reference dropout 0.1, and predicts absolute EEF/action-space targets. It is intended as the paired reference-video counterpart to the mask-v3 no-ref baseline with the same model backbone, action stream, target robot, train20 task split, and 100k training horizon.

## Files

```text
step-100000.safetensors   joint VAM checkpoint at 100k steps
training_metadata.json    training and upload metadata
README.md                 this model card
```

Checkpoint SHA256:

```text
3c1060b425866afc60fc37136973fa36f5d6803c49468f6b7be5be2967e57fe8
```

The checkpoint contains both fine-tuned Wan video DiT weights and `action_dit.*` ActionMoT weights. The Wan2.2-TI2V-5B base assets are not bundled.

## Training Summary

```text
run directory:       src/vam/models/train/vam_icl/paired_v3_alltasks_mv_mot_ti2v5b_16g_100k_mask_v3_ref_openwam_eef_fastwam_warmstart_refdrop10_0524_0329
wandb run:           run-20260524_113637-h7w9s4pf
wandb run name:      paired_v3_train_mv_mot_ti2v5b_16g_100k_mask_v3_openwam_refdrop10_0524_0329
wandb url:           https://wandb.ai/wuji-tech/vam_icl/runs/h7w9s4pf
checkpoint:          step-100000.safetensors
dataset:             RoboTwin ICL-paired v3
target robot:        arx-x5
task split:          20 train tasks
episodes/task:       150 train episodes
backbone:            Wan2.2-TI2V-5B
action stream:       30-layer ActionMoT, OpenWAM/FastWAM warm-start style
reference mode:      enabled, cross-embodiment full reference video
reference dropout:   0.1
multiview:           enabled, RoboTwin head + left wrist + right wrist layout
mask variant:        v3
```

## Action And Proprio Contract

```text
action_space:     eef
proprio_space:    action
action_dim:       16
proprio_dim:      16
action_format:    absolute
```

The model predicts absolute 16-D EEF/action-space targets. Use the matching local EEF/action normalization stats from the OpenWAM EEF training setup.

## Temporal And Visual Contract

```text
raw action window:          33 frames
action horizon:             32 actions
action_video_freq_ratio:    4
target video frames:        9 frames sampled at raw indices [0, 4, ..., 32]
resolution:                 384 x 320
resize mode:                stretch
multiview layout:           head camera on top, left/right wrist cameras below
full reference video:       enabled
max reference frames:       41
reference subsample factor: 4
paired reference:           false
```

For closed-loop eval, use the matching local VAM code path with:

```text
action_dim=16
proprio_dim=16
num_frames=9
action_horizon=32
height=384
width=320
multiview=true
action_space=eef
proprio_space=action
disable_reference_video=false
full_reference_video=true
max_ref_frames=41
ref_subsample_factor=4
mask_variant=v3
reference_dropout=0.1
```

## Validation Snapshot

Last validation line observed for this checkpoint:

```text
[val_id step 100000] 20/98741 samples: loss=0.143614, loss_video=0.101261, loss_action=0.042352
[val_id step 100000] task=grab roller action_MSE=0.000742 action_MAE=0.020227 video_MSE=1894.89 PSNR=19.31 SSIM=0.8012 LPIPS=0.1998
```

These numbers are a training-time validation snapshot, not a full deployment evaluation.

## Notes

This checkpoint was trained for research use in the local VAM codebase. It expects the matching model architecture and RoboTwin observation/action conventions used by `src/vam/examples/wanvideo/human2robot/train_video_action.py`.