KhalilGao/DOMINO_absolute_motion_v2

DOMINO Absolute Motion v2

DOMINO_absolute_motion_v2 is the complete packed training corpus for DynamicWAM's exact-simulator-time motion pipeline. It is a training-ready derivative of H-EmbodVis/DOMINO, not a copy of the raw RGB dataset.

Every sample was packed as one aligned record containing video latents, action/state targets, frame indices, a language-group identifier, and four history intervals of absolute motion descriptors. The alignment is fixed at conversion time; training does not join independent sidecars at runtime.

Dataset summary

The packed corpus contains all 1,428,327 valid samples. The production training contract uses episode_balanced_without_replacement, selecting 10 distinct samples from each episode per epoch with seed 2026 (105,000 samples per epoch).

Stored tensors

Each main .safetensors shard contains up to 10,000 aligned samples. Shapes below exclude the leading sample dimension.

The language bank stores 3,500 precomputed 4096-dimensional UMT5-XXL entries in bfloat16, grouped as 100 entries for each of the 35 tasks.

Absolute-motion contract

Motion is computed from the head view on a configured 64 x 64 flow grid. Five history frames define four intervals. Interval duration comes from domino_schema_v2.sim_time_seconds, rather than an assumed fixed frame rate. Each interval has 12 descriptors:

1. 1.mean x displacement;
2. 2.mean y displacement;
3. 3.mean displacement magnitude;
4. 4.p99 displacement magnitude;
5. 5.elapsed time;
6. 6.mean x velocity;
7. 7.mean y velocity;
8. 8.mean speed;
9. 9.p99 speed;
10. 10.mean x acceleration;
11. 11.mean y acceleration;
12. 12.mean acceleration magnitude.

motion_stats.json pins normalization statistics and action_stats.json pins action statistics. The precise optical-flow, timestamp, sampling, and normalization contracts are recorded in dataset.json.

File layout

.
├── dataset.json
├── stats.json
├── action_stats.json
├── motion_stats.json
├── episodes.jsonl
├── samples.jsonl
├── shards/
│   ├── shard_000000.safetensors
│   ├── shard_000000.json
│   └── ...
├── lang/
│   ├── lang.json
│   └── shards/
│       ├── shard_000000.safetensors
│       ├── shard_000000.json
│       └── ...
├── SHA256SUMS
├── PAYLOAD_MANIFEST.json
└── VALIDATION.md

The JSON sidecar beside each tensor shard gives the exact keys, shapes, dtypes, sample range, and byte size for that shard.

Download

Install the current Hugging Face CLI with Xet support, then download the repository:

hf download KhalilGao/DOMINO_absolute_motion_v2 \
  --repo-type dataset \
  --local-dir DOMINO_absolute_motion_v2

The repository is approximately 311 GiB. Verify the immutable payload after download:

cd DOMINO_absolute_motion_v2
sha256sum -c SHA256SUMS

SHA256SUMS covers the 406 source payload files. Repository documentation files are intentionally outside that payload manifest. The SHA-256 of SHA256SUMS itself is 705371585c4fdb29440a5a49849a69e94fd4c85acda63c2a4ed005aa865bad2c.

DynamicWAM loader

With the DynamicWAM source tree on PYTHONPATH:

from dynamicwam.training.data import PackedAbsoluteMotionDataset

dataset = PackedAbsoluteMotionDataset(
    "DOMINO_absolute_motion_v2",
    max_open_shards=8,
)
sampler = dataset.make_sampler(
    samples_per_episode=10,
    seed=2026,
)

The loader validates the format version, dataset fingerprint, motion contract, motion statistics, action statistics, and sampler contract before training.

Tasks

The dataset contains the following DOMINO Level-1 tasks:

adjust_bottle, beat_block_hammer, click_alarmclock, click_bell, dump_bin_bigbin, grab_roller, handover_block, handover_mic, hanging_mug, move_can_pot, move_pillbottle_pad, move_playingcard_away, move_stapler_pad, place_a2b_left, place_a2b_right, place_bread_basket, place_bread_skillet, place_can_basket, place_container_plate, place_empty_cup, place_fan, place_mouse_pad, place_object_basket, place_object_scale, place_object_stand, place_phone_stand, place_shoe, press_stapler, put_bottles_dustbin, put_object_cabinet, rotate_qrcode, scan_object, shake_bottle, shake_bottle_horizontally, and stamp_seal.

Provenance

• —Raw source: H-EmbodVis/DOMINO
• —Video latent model: Wan-AI/Wan2.2-TI2V-5B
• —Text encoder: google/umt5-xxl
• —Conversion-manifest digest recorded by the packer: 926ae37efced8fec3f1a59c4fe2930fbb9bca4c65197efe9e9ba80aa9fabdb16
• —Action-statistics SHA-256: 730a91e41b6a23c70cec49cd222835373ba9364680da5753f4408c5534319b04
• —Motion-statistics SHA-256: 7a2109f0d93418e9bc3b5c1c6c9961ac7afbda61f672a153b999e8df385a01b4

No Wan2.2 or UMT5 model weights are included in this repository.

Limitations

• —This is packed simulated training data for the 35 DOMINO Level-1 tasks; it does not contain real-robot data.
• —Raw RGB observations are not included. Use the upstream DOMINO repository when raw episodes are required.
• —The stored latents and language embeddings are tied to the encoder configurations recorded in dataset.json; changing encoders requires repacking.
• —This custom shard format is optimized for training and is not a standard tabular Hugging Face Dataset, so the web Data Viewer may not render sample rows.
• —The corpus does not by itself reproduce the complete DynamicWAM software, model checkpoints, or evaluation environment.

License and citation

This processed dataset is released under Apache License 2.0, consistent with the upstream DOMINO dataset and the encoder repositories listed above. See LICENSE for the full text.

If you use this artifact, cite the accompanying DynamicWAM work and the upstream DOMINO paper:

@inproceedings{fang2026towards,
  title     = {Towards Generalizable Robotic Manipulation in Dynamic Environments},
  author    = {Fang, Heng and Li, Shangru and Wang, Shuhan and Xi, Xuanyang and Liang, Dingkang and Bai, Xiang},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}