DW05-Robotwin

DW05-Robotwin

DW05-Robotwin is a released DW05 world-action model checkpoint for RobotWin-style robot policy inference and video rollout. It predicts future robot actions and can generate future robot-view video through the Dexbotic DW05 runtime.

The repository is packaged as a DW05 runtime bundle. Users should point the runtime to this repository root; they do not need to reproduce upstream model cache directory names.

What Is Included

The core checkpoint is:

model.pt

For RobotWin policy inference, the runtime also needs normalization statistics. The recommended release layout is:

DW05-Robotwin/
  model.pt
  norm_stats.json

If norm_stats.json is not included in a particular release snapshot, provide the matching RobotWin normalization statistics explicitly through the runtime --stats / --norm-stats-path argument.

This release is organized as a full offline runtime bundle:

DW05-Robotwin/
  model.pt
  norm_stats.json
  vae/
    model.pth
  text_encoder/
    model.pth
  tokenizer/
    tokenizer_config.json
    tokenizer.json
    spiece.model
    special_tokens_map.json

vae/, text_encoder/, and tokenizer/ are DW05-facing bundle directories. They contain upstream-compatible runtime components, but the user-facing package layout remains DW05-owned.

Intended Runtime

Use this checkpoint with the Dexbotic DW05 runtime:

git clone https://gitlab.dexmal.com/robotics/dexbotic-open.git dexbotic
cd dexbotic
pip install -e .

Set the bundle root:

export DW05_MODEL_BASE_PATH=/path/to/DW05-Robotwin
export TOKENIZERS_PARALLELISM=false

If norm_stats.json is not placed at the bundle root, pass its path explicitly with --stats, --norm-stats-path, or the corresponding Dexbotic config field.

Online Demo

Run the RobotWin online demo from the Dexbotic repository:

python playground/online_demos/robotwin_online_demo.py --web \
  --ckpt /path/to/DW05-Robotwin/model.pt \
  --stats /path/to/DW05-Robotwin/norm_stats.json \
  --model_base_path /path/to/DW05-Robotwin \
  --device cuda:0 \
  --num_inference_steps 5

The demo exposes the original interactive RobotWin joint-condition UI and uses the shared DW05RobotWinPolicy runtime.

Programmatic Policy Loading

from dexbotic.policy.dw05_policy import DW05RobotWinPolicy, DW05RobotWinPolicyConfig

policy = DW05RobotWinPolicy(
    DW05RobotWinPolicyConfig(
        checkpoint_path="/path/to/DW05-Robotwin/model.pt",
        norm_stats_path="/path/to/DW05-Robotwin/norm_stats.json",
        model_base_path="/path/to/DW05-Robotwin",
        device="cuda:0",
        mixed_precision="bf16",
        num_inference_steps=5,
    )
)

The policy expects RobotWin-style observations with RGB camera images, robot state, and a natural-language instruction. See the Dexbotic DW05 README for the complete runtime, evaluation, and deployment examples.

File Notes

• model.pt: DW05 trained checkpoint. It contains the DW05 world-action model parameters used by the released policy, including trained video/action/MoT weights and the proprio encoder.
• norm_stats.json: action/state normalization statistics used by RobotWin policy inference. This is required for action normalization and denormalization.
• vae/: local VAE runtime component for image/video latent encoding and decoding.
• text_encoder/: local text encoder runtime component for prompt encoding.
• tokenizer/: local tokenizer files for prompt tokenization.

License And Attribution

This DW05-Robotwin release is distributed under the Apache License 2.0. See LICENSE for the full license text and NOTICE for third-party attribution.

This release is trained from and used with open third-party components, including Wan2.2, uMT5-compatible tokenizer/text components, and RoboTwin/RobotWin-style data and evaluation. Those components remain subject to their own upstream licenses and attribution requirements.

In particular:

• Wan2.2 components are licensed upstream under Apache License 2.0.
• uMT5 tokenizer/text components are licensed upstream under Apache License 2.0.
• RoboTwin code and public dataset metadata were observed under MIT License.

Users who redistribute a modified bundle or include additional third-party files should preserve the corresponding upstream license and attribution notices.

Limitations

• The checkpoint is released for research and development of world-action models, robot policy inference, and video rollout experiments.
• Real-robot deployment requires independent safety validation, robustness evaluation, and environment-specific testing.
• The model expects preprocessing compatible with the Dexbotic DW05 RobotWin runtime, including image composition, state/action normalization, and prompt formatting.
• Performance outside RobotWin-style observations and task distributions has not been guaranteed.

Troubleshooting

Model components are not found.

Set DW05_MODEL_BASE_PATH or pass --model_base_path to the DW05 runtime. The path should be the root of this DW05 bundle.

Norm stats are missing.

Place norm_stats.json at the bundle root or pass its path explicitly with --stats / --norm-stats-path.

The online demo starts but generation looks misaligned.

Check that the runtime uses the Dexbotic DW05 preprocessing path: RobotWin image composition, DW05 normalization statistics, and the matching checkpoint should be used together.

Citation

If you use DW05-Robotwin, please cite or acknowledge DW05/Dexbotic and the upstream projects listed in NOTICE, including Wan2.2, uMT5, and RoboTwin where applicable.