Ego4WAM-Joint

Released weights for the Joint variant of Ego4WAM.

Contents

File	Size	Description
model.safetensors	25.07 GiB	2,092 BF16 tensors containing the complete framework state dict
inference_config.yaml	<1 KiB	Minimal model selection: Ego4WAM with interaction_mode: joint
model_assets/	20.46 MiB	VAE/text-encoder configs and the UMT5 tokenizer required for offline construction
LICENSE		Apache-2.0 license for the released weights

The checkpoint contains the Video DiT, VAE, UMT5 text encoder, Action DiT, and proprioceptive encoder weights. No additional backbone weights are downloaded when loading this release.

Loading

from huggingface_hub import snapshot_download

from starVLA.model.framework.base_framework import baseframework

root = snapshot_download("HorizonRobotics/Ego4WAM")
model = baseframework.from_pretrained(f"{root}/model.safetensors")

This constructs the framework from inference_config.yaml, resolves the local files in model_assets/, and loads the state dict with strict=True.

Model geometry

Tensors / dtype	2,092 / BF16
Parameters	13,456,608,092
State-dict prefixes	backbone. 1,266; action_model. 824; proprio_encoder. 2
Interaction mode	Joint synchronous video/action attention
State / action dimension	32 / 32
Action horizon	32
RoboDojo execution output	First 14 dimensions after inverse normalization
MoT	30 layers; video hidden size 3,072; action hidden size 1,024
Sampling	20-step Euler flow matching
Camera input	Head, left wrist, and right wrist RGB views

The SHA-256 digest of model.safetensors is:

ae1b4595e1909e4573ad3214e1c5112f1920a34675340f6adfd4f2e9ec6c12c4

License and attribution

The released weights are provided under Apache-2.0. Ego4WAM source code is provided under MIT. Ego4WAM builds on StarVLA and uses Wan2.2 TI2V-5B and UMT5-XXL components; retain the corresponding upstream attribution when redistributing derivative work.