DW05-Base

DW05-Base is a released DW05 base world-action model checkpoint for the Dexbotic DW05 runtime. It contains a trained DW05 video/action/MoT checkpoint with a 32-dimensional action and proprioception interface, packaged together with local runtime components for offline loading.

This repository is a DW05 runtime bundle. Users should point Dexbotic to the repository root with DW05_MODEL_BASE_PATH; they do not need to reproduce upstream model cache directory names.

What Is Included

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

The base checkpoint is intended as a general DW05 model release and as a starting point for method development, fine-tuning, and downstream policy/runtime integration. It is not packaged with RobotWin policy normalization statistics. For RobotWin policy inference, use a downstream RobotWin-specific DW05 checkpoint and its matching norm_stats.json.

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-Base
export TOKENIZERS_PARALLELISM=false

Checkpoint Notes

• model.pt: DW05 base checkpoint. The checkpoint contains trained video/action/MoT weights and a proprio encoder.
• Action dimension: 32.
• Proprioception dimension: 32.
• Stored dtype metadata: torch.bfloat16.
• Training step recorded in the checkpoint: 140000.

Runtime Components

• 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.

These are DW05-facing bundle directories. They contain upstream-compatible runtime components, but the user-facing package layout remains DW05-owned.

Example Loading

import torch

from dexbotic.model.dw05 import DW05ModelConfig

model_cfg = DW05ModelConfig(
    load_text_encoder=True,
    skip_dit_load_from_pretrain=True,
    action_dim=32,
    proprio_dim=32,
)

model = model_cfg.build_model(model_dtype=torch.bfloat16, device="cuda:0")
model.load_checkpoint("/path/to/DW05-Base/model.pt")
model.eval()

For downstream policy inference, make sure the policy preprocessing, action/proprio dimensions, and normalization statistics match the checkpoint you load.

Relationship To DW05-Robotwin

DW05-Base is a general 32D base checkpoint. DW05-Robotwin is a downstream RobotWin-oriented release with RobotWin policy normalization assets and a RobotWin-specific runtime configuration. Use DW05-Robotwin for the packaged RobotWin online demo and policy evaluation path.

License And Attribution

This DW05-Base 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 and uMT5-compatible tokenizer/text components. 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.

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 DW05-style world-action models and downstream fine-tuning.
• It is not a drop-in RobotWin policy checkpoint unless paired with compatible policy preprocessing and normalization statistics.
• Real-robot deployment requires independent safety validation, robustness evaluation, and environment-specific testing.
• Performance outside the training and downstream fine-tuning distributions has not been guaranteed.

Troubleshooting

Runtime 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.

Action dimension mismatch.

Build the DW05 model with action_dim=32 and proprio_dim=32 before loading this checkpoint.

RobotWin policy output is misaligned.

Use a RobotWin-specific DW05 checkpoint and matching norm_stats.json, or fine-tune this base checkpoint with the intended RobotWin action/state preprocessing.

Citation

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