HWM-0-Preview-RoboDojo

HWM-0-Preview combines a vision-language subtask planner, visual and semantic memory, and joint video/action flow matching for dual-arm manipulation. This repository contains the complete RoboDojo inference bundle exported from the 60,000-step EMA checkpoint.

Run this model through the HWM_0_preview adapter in XPolicyLab, which provides policy serving and RoboDojo evaluation. This repository supplies the weights and runtime assets used by that adapter.

Contents

File	Size	Description
model.pt	31.97 GiB	Complete BF16 model state dictionary
config.yaml	5.06 KiB	Architecture, conditioning, history and inference settings
dataset_statistics.json	5.14 KiB	Required state/action normalization statistics
processor/	19.08 MiB	VLM processor, tokenizer and chat template
tokenizer/	16.06 MiB	T5 tokenizer assets
manifest.json	1.11 KiB	Export checksums and configuration fingerprint

HWM-0-Preview-RoboDojo/
├── README.md
├── .gitattributes
├── model.pt
├── config.yaml
├── dataset_statistics.json
├── manifest.json
├── processor/
└── tokenizer/

The checkpoint includes the VLM, T5 encoder, VAE, Video DiT, Action DiT, state projection and learned queries. The matching adapter constructs these components from the bundled configuration without downloading additional backbone weights. Both tokenizers load from local JSON assets.

Usage with XPolicyLab

Follow the official XPolicyLab guide for setup, checkpoint conventions and deployment, and the policy/HWM_0_preview/README.md in the matching source release for this model's installation requirements. Use a checkout that includes the HWM_0_preview adapter. Keep the policy environment separate from the RoboDojo simulator/client environment, as recommended by XPolicyLab.

Download the bundle

From the XPolicyLab checkout, enter the adapter directory. With the Hugging Face CLI available, download the complete bundle into HWM-0-Preview-RoboDojo/:

cd policy/HWM_0_preview
hf download "HorizonRobotics/HWM-0-Preview-RoboDojo" \
  --local-dir HWM-0-Preview-RoboDojo

The adapter also provides download_checkpoint.sh, which defaults to current repo:

bash download_checkpoint.sh

Both commands use HWM-0-Preview-RoboDojo/ inside the adapter directory. Keep the entire bundle together, including config.yaml, normalization statistics, processor/ and tokenizer/.

Evaluate through the standard entrypoint

After installing the policy and evaluation environments according to their respective guides, run from the same adapter directory:

# Replace these with your installed environment names and available GPU IDs.
POLICY_ENV=hwm_policy
EVAL_ENV=robodojo
POLICY_GPU=0
ENV_GPU=1

EVAL_ENV_TYPE=sim bash eval.sh \
  RoboDojo stack_bowls ./HWM-0-Preview-RoboDojo arx_x5 joint 0 \
  "$POLICY_GPU" "$ENV_GPU" "$POLICY_ENV" "$EVAL_ENV"

eval.sh starts the policy server, waits for it to become ready, runs the environment client and cleans up the server. The adapter resolves the checkpoint and loads the model and its assets. Change stack_bowls to the desired task. The ckpt_name argument is the explicit path ./HWM-0-Preview-RoboDojo, so different evaluation seeds use the same downloaded bundle.

Use EVAL_ENV_TYPE=debug for an offline interface check; it does not measure task success. For simulation, follow the official RoboDojo setup and XPolicyLab workspace layout. For split-machine deployment, follow XPolicyLab's Deployment Flow using setup_eval_policy_server.sh on the policy machine and setup_eval_env_client.sh on the simulator machine. Refer to those official guides for argument details and environment setup.

The model requires approximately 32 GiB for weights alone, plus memory for activations and caches. Select a policy GPU with sufficient headroom; prior integration validation used H20 hardware. Model-specific inference settings are configured through the adapter's deploy.yml: num_inference_steps, flow_shift and replan_steps inherit bundle defaults of 10, 5.0 and 30 when set to null. inference_batch_size defaults to 4; reduce it for lower GPU memory use.

Model geometry

Item	Value
Checkpoint	60,000-step EMA
Tensors / dtype	3,078 / BF16
Stored tensor elements	17,162,844,810
Robot	ARX X5 dual arm
State / action dimension	14 / 14
Action horizon	48 absolute joint-position targets
Execution horizon	30 control steps per replan
Sampling	10 flow-matching steps; flow shift 5.0
Camera input	Head, left-wrist and right-wrist RGB
Visual history	Up to 20 frames, sampled every 30 control steps
Normalization	Bundled mean/std statistics, key new_embodiment

Integrity

model.pt is 34,326,694,240 bytes. Its SHA256 digest is:

40d998b67d27824fab5c6b64b4f20c2926d299dac9393dadb07a8c831a226988

The remaining original bundle checksums are recorded in manifest.json. Exported weights, configuration, normalization statistics and tokenizer assets are preserved byte for byte.

This release contains inference weights and assets. Training state and benchmark results are not included; no complete benchmark score is claimed here.