---
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.