This is the EEF reference-video variant: during training the model used cross-embodiment full reference videos, with reference dropout 0.3, and predicts absolute EEF/action-space targets. It is intended as the paired reference-video counterpart to the EEF 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
model_config.json         architecture and data/preprocessing contract
training_config.yaml      training configuration snapshot
action_stats.npy          EEF/action normalization stats used by training
training_log_node0.txt    rank/node 0 training log
training_log_node1.txt    rank/node 1 training log
README.md                 this model card

Checkpoint SHA256:

1ee0863eb4a7096bd90bdd4fd744d336f496688edf58036cf635fccfe96cd937

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

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run directory:       src/vam/models/train/vam_icl/paired_v3_alltasks_mv_mot_ti2v5b_16g_100k_v2_ref_openwam_eef_fastwam_warmstart_refdrop03_relaunch_20260521-234642
wandb run:           run-20260522_075220-32gb6xg7
wandb run name:      paired_v3_train_mv_mot_ti2v5b_16g_100k_mask_v2_openwam_0522_0748
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:   0.3
multiview:           enabled, RoboTwin head + left wrist + right wrist layout
mask variant:        v2

Action And Proprio Contract

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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 included action_stats.npy for normalization/denormalization.

Temporal And Visual Contract

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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

Validation Snapshot

Last validation line observed for this checkpoint:

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[val_id step 100000] 20/98741 samples: loss=0.096612, loss_video=0.087265, loss_action=0.009346
[val_id step 100000] task=grab roller action_MSE=0.008076 action_MAE=0.025867 video_MSE=2476.98 PSNR=18.62 SSIM=0.7764 LPIPS=0.2472

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.

knightnemo/robotwin-icl-v3-arx-x5-vam-ti2v5b-openwam-eef-ref-train20-100k

RoboTwin ICL v3 ARX-X5 VAM TI2V-5B OpenWAM EEF Ref Train20 100K

Files

Training Summary

Action And Proprio Contract

Temporal And Visual Contract

Validation Snapshot

Notes

knightnemo/
robotwin-icl-v3-arx-x5-vam-ti2v5b-openwam-eef-ref-train20-100k