README.md

---
license: apache-2.0
language:
- en
pretty_name: DOMINO Absolute Motion v2
size_categories:
- 1M<n<10M
tags:
- robotics
- robot-learning
- imitation-learning
- video-latents
- optical-flow
- safetensors
- domino
- dynamicwam
---

# 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](https://huggingface.co/datasets/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

| Property | Value |
|---|---:|
| Tasks | 35 |
| Episodes | 10,500 (300 per task) |
| Packed samples | 1,428,327 |
| Main tensor shards | 143 |
| Language-bank entries | 3,500 |
| Language tensor shards | 55 |
| Payload files | 406 |
| Payload size | 333,217,057,616 bytes (310.33 GiB) |
| Format | `dynamicwam_absolute_motion_dataset` |
| Format version | 2 |
| Dataset fingerprint | `2feac8645817e3a896afd855341130f23a6d92bfd28917f4cb3e2dbb4eb68154` |

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.

| Key | Stored dtype | Per-sample shape |
|---|---|---|
| `condition_latents` | `float32` | `[48, 2, 24, 20]` |
| `future_latents` | `float32` | `[48, 2, 12, 10]` |
| `action_sequences` | `float32` | `[16, 14]` |
| `initial_states` | `float32` | `[14]` |
| `absolute_motion_features` | `float32` | `[4, 12]` |
| `absolute_motion_interval_valid_masks` | `bool` | `[4]` |
| `absolute_motion_acceleration_valid_masks` | `bool` | `[4]` |
| `action_indices` | `int64` | `[16]` |
| `video_indices` | `int64` | `[8]` |
| `condition_frame_indices` | `int64` | scalar |
| `episode_indices` | `int64` | scalar |
| `lang_group_ids` | `int64` | scalar |
| `sample_ids` | `int64` | scalar |

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

```text
.
├── 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:

```bash
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:

```bash
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`:

```python
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](https://huggingface.co/datasets/H-EmbodVis/DOMINO)
- Video latent model: [Wan-AI/Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B)
- Text encoder: [google/umt5-xxl](https://huggingface.co/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:

```bibtex
@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}
}
```