File size: 3,448 Bytes
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license: apache-2.0
library_name: pytorch
tags:
- robotics
- robot-manipulation
- world-model
- imitation-learning
- video-policy
- optical-flow
- domino
- dynamicwam
---
# DynamicWAM
Official DynamicWAM checkpoints for DOMINO manipulation. This repository is
organized by paper-facing model variant; each checkpoint is paired with its
action-normalization statistics and immutable configuration snapshots.
## Released variants
| Paper name | Checkpoint | Motion conditioning | DOMINO Level 1 |
|---|---|---|---:|
| **DynamicWAM (w/o motion)** | [`checkpoints/dynamicwam_wo_motion/model.pt`](checkpoints/dynamicwam_wo_motion/model.pt) | Four history Flow-RGB intervals; **no explicit absolute-motion descriptor tokens** | 953/3,500, SR 27.23%, MS 41.6244 |
| **DynamicWAM** | [`checkpoints/dynamicwam/model.pt`](checkpoints/dynamicwam/model.pt) | History Flow RGB + exact-time absolute-motion descriptors | 1,337/3,500, SR 38.20%, MS 53.16 |
“w/o motion” refers specifically to removing the explicit numeric
absolute-motion/kinematic descriptor branch. It does **not** mean that optical
flow is removed: the variant still consumes history Flow RGB.
The reported numbers are the archived 35-task × 100-episode DOMINO Level 1
system results. They are not a newly rerun benchmark and should not be
interpreted as a matched one-variable causal estimate beyond the named model
variants.
## Repository layout
```text
checkpoints/
├── dynamicwam_wo_motion/
│ ├── model.pt
│ └── action_stats.json
└── dynamicwam/
├── model.pt
└── action_stats.json
configs/
├── dynamicwam_wo_motion/
│ ├── stage1_video.yaml
│ ├── stage2_action.yaml
│ ├── stage3_joint.yaml
│ └── deploy.yaml
└── dynamicwam/
└── absolute_motion_v2.yaml
MODEL_MANIFEST.json
SHA256SUMS
```
The `dynamicwam_wo_motion` YAML files are exact historical configuration
snapshots and therefore retain their original cluster paths. Remap those paths
to your local checkout and downloaded assets before training or evaluation.
## Download
```bash
hf download KhalilGao/DynamicWAM \
--include "checkpoints/dynamicwam_wo_motion/*" \
--include "configs/dynamicwam_wo_motion/*" \
--local-dir external/DynamicWAM
```
The matching packed training corpus is published at
[`KhalilGao/DynamicWAM-data`](https://huggingface.co/datasets/KhalilGao/DynamicWAM-data)
under `dynamicwam_wo_motion/`.
## Checkpoint identity
| Variant | Global step | Size (bytes) | SHA-256 |
|---|---:|---:|---|
| DynamicWAM (w/o motion) | 40,000 | 1,976,638,827 | `fa88a1f33f2205db5a4534e09bb02d1bee9dea7b2049299ac68932a34de82f03` |
| DynamicWAM | 40,000 | 1,977,857,939 | `7c0dfc44a785ea1f6bd1f833f09dcadc2e470dadb1ba5508fa98918e147671d7` |
Both are native PyTorch checkpoint dictionaries. As with any pickle-backed
PyTorch artifact, load them only from a trusted revision and verify the hashes
in `SHA256SUMS` first.
## External assets
The checkpoints do not bundle Wan2.2-TI2V-5B, its tokenizer/VAE, the DOMINO
runtime, CuRobo, or simulator assets. See the
[`DynamicWAM` source repository](https://github.com/Autumn1337/DynamicWAM)
for environment and evaluation setup.
## License
Project-owned release material is provided under Apache-2.0. Upstream assets
retain their own licenses; this repository does not relicense or redistribute
the external runtime dependencies listed above.
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