KhalilGao/DynamicWAM-data

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, 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.

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

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

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.

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.