---
pretty_name: DynamicWAM Training Data
license: apache-2.0
language:
- en
task_categories:
- robotics
size_categories:
- 1M<n<10M
tags:
- robot-manipulation
- imitation-learning
- video-latents
- optical-flow
- safetensors
- domino
- dynamicwam
---

# DynamicWAM training data

This repository contains the packed training corpus for **DynamicWAM (w/o
motion)**, the paper variant previously tracked internally as B0. The public
name is now consistently **DynamicWAM (w/o motion)**.

The corpus is a lossless packed derivative of
[`H-EmbodVis/DOMINO`](https://github.com/H-EmbodVis/DOMINO), not a copy of the
raw RGB dataset. It stores aligned video latents, action/state targets, history
Flow RGB, language embeddings, frame indices, and metadata. The Hub layout
buckets the language shards to satisfy Hugging Face's per-directory file
limit; a supplied materialization tool creates the flat compatibility view
expected by the released training loader without duplicating payload bytes.

## Important naming distinction

“w/o motion” means **without the explicit numeric absolute-motion descriptor
tokens** introduced by the full DynamicWAM model. This dataset still contains
four history optical-flow intervals represented as Flow RGB. Its temporal
contract is `model_policy_index_aligned_v1`; physical simulator timestamps are
not available in this earlier corpus.

For the later exact-simulator-time corpus used by full DynamicWAM, use
[`KhalilGao/DOMINO_absolute_motion_v2`](https://huggingface.co/datasets/KhalilGao/DOMINO_absolute_motion_v2).

## Dataset summary

| Field | Value |
|---|---:|
| Tasks | 35 |
| Episodes | 26,019 |
| Clean episodes | 5,207 |
| Randomized episodes | 20,812 |
| Packed samples | 3,455,662 |
| Main data shards | 346 |
| Payload files | 82,008 |
| Logical payload size | 2,199,796,269,773 bytes (2.00 TiB) |
| Zero-byte files | 0 |
| Dataset fingerprint | `bc20355aa094585499c2ade05d8bcfdb1f082aabc73259ebcded694e3301c425` |

## Repository layout

```text
dynamicwam_wo_motion/
├── dataset.json
├── stats.json
├── action_stats.json
├── episodes.jsonl
├── samples.jsonl
├── shards/
│   ├── shard_*.safetensors
│   └── shard_*.json
└── lang/
    ├── lang.json
    └── shards/
        ├── bucket_000/
        │   ├── shard_000000.safetensors
        │   └── ...
        └── bucket_040/
            └── shard_040654.json
DATASET_RELEASE.json
FILE_LAYOUT.json
PAYLOAD_MANIFEST.jsonl
SHA256SUMS
tools/materialize_training_layout.py
```

This is a custom Safetensors-backed training format, so the Hugging Face
Dataset Viewer is not the authoritative loader. Use the DynamicWAM dataset
loader from the source repository.

## Download

```bash
hf download KhalilGao/DynamicWAM-data \
  --repo-type dataset \
  --local-dir data/DynamicWAM-data

python data/DynamicWAM-data/tools/materialize_training_layout.py \
  data/DynamicWAM-data/dynamicwam_wo_motion
```

The second command creates relative symlinks such as
`lang/shards/shard_000000.safetensors ->
bucket_000/shard_000000.safetensors`. It is idempotent. Use `--mode hardlink`
when symlinks are unsuitable; neither mode duplicates the underlying payload.

The matching checkpoint is
[`KhalilGao/DynamicWAM/checkpoints/dynamicwam_wo_motion/model.pt`](https://huggingface.co/KhalilGao/DynamicWAM/blob/main/checkpoints/dynamicwam_wo_motion/model.pt).

## Integrity and provenance

- Format: `efficient_wam_train_dataset`, version 2.
- Action/state dimension: 14; action chunk size: 16.
- Future latent sequence: 8 frames; composite video size: 384×320.
- History flow: head view, four intervals, 64×64 Farneback flow, P99
  normalization, fixed raw stride.
- The packaged action statistics have SHA-256
  `730a91e41b6a23c70cec49cd222835373ba9364680da5753f4408c5534319b04`,
  exactly matching the released DynamicWAM (w/o motion) checkpoint.
- `PAYLOAD_MANIFEST.jsonl` maps every native logical path to its bucketed Hub
  path and records byte size and source modification time.
- `SHA256SUMS` is generated from the upload-time content hashes and covers the
  complete bucketed payload under `dynamicwam_wo_motion/`.

## License

The packaged derivative is released under Apache-2.0, consistent with the
upstream DOMINO data/code release. Language and video-model-derived features
remain subject to the applicable upstream model terms; no external model
weights are included here.