Ego4WAM

What Matters When Scaling Egocentric Human Data for Robot Learning?

Paper · Project page

Ego4WAM studies how human-robot alignment, data duration, task diversity and available supervision shape the value of egocentric human data for robot learning using a unified world-action model. This repository contains complete model bundles for video-pretrained initialization and RoboDojo deployment. Use the Ego4WAM-enabled source release; the Python import namespace remains starVLA. These checkpoints are not standard Transformers AutoModel bundles.

Models

Model	Interaction	Intended use
Ego4WAM_VideoPretrained	Base	Video-only initialization for Base or Joint RoboDojo Post-training; not a robot-action-posttrained policy
Ego4WAM_VideoPretrained_RoboDojoJoint	Joint	Video-pretrained, RoboDojo-posttrained policy for inference and evaluation

The Joint weights are unchanged from the initial root-directory release. Select a model directory explicitly; there is no default root checkpoint in this collection. Wan-initialized RoboDojo checkpoints and video-pretrained RoboDojo Base weights are not included in this release.

Bundle layout

Ego4WAM/
├── README.md
├── LICENSE
├── .gitattributes
├── Ego4WAM_VideoPretrained/
│   ├── README.md
│   ├── LICENSE
│   ├── inference_config.yaml
│   ├── model.safetensors
│   └── model_assets/
└── Ego4WAM_VideoPretrained_RoboDojoJoint/
    └── the same five-item bundle layout

Each checkpoint contains its Video DiT, Action DiT, state projection, VAE and UMT5 weights. model_assets/ bundles the VAE/text-encoder configurations and tokenizer. Additional Wan/VAE/T5 weights do not need to be downloaded when loading these bundles. RoboDojo normalization and kinematic assets are provided by the code release, not by an internal experiment manifest.

Download and load a single model

Use allow_patterns to avoid downloading both large checkpoints:

from huggingface_hub import snapshot_download
from starVLA.model.framework.base_framework import baseframework

variant = "Ego4WAM_VideoPretrained_RoboDojoJoint"
root = snapshot_download(
    "HorizonRobotics/Ego4WAM",
    allow_patterns=[f"{variant}/**"],
)
model_dir = f"{root}/{variant}"
model = baseframework.from_pretrained(f"{model_dir}/model.safetensors")

This reads the adjacent inference_config.yaml and model_assets/, constructing and loading the model with strict=True. Set CKPT to model_dir when using the RoboDojo policy-server scripts. The model's public configuration exposes only framework.name: Ego4WAM and framework.interaction_mode: base|joint.

For video-pretrained initialization, select Ego4WAM_VideoPretrained instead. Set EGO4WAM_PRETRAINED_DIR to that downloaded model directory for the Base or Joint RoboDojo Post-training scripts. See its model card for the Joint initialization override; do not substitute the already-posttrained Joint policy.

The initial flat release remains available at revision 262306ba29e9a4581ac5f714b90711498917444f for existing pinned download commands. New downloads from main should use the model-specific paths above.

RoboDojo evaluation

The Joint policy uses 32-D state/action representations, a 32-step action horizon and a fixed 20-step Euler sampler. The server converts predictions to the 14-D RoboDojo joint-control output. Inputs are head, left-wrist and right-wrist RGB observations plus state and environment geometry.

Use examples/simBenchmarks/RoboDojo/eval_files_ego4wam/ and docs/RoboDojo.md in the source release for the pinned RoboDojo version used in the paper, dependency setup and local launch commands. This release does not target the current official leaderboard/submission protocol or the newer synchronized-data retraining. A one-task smoke does not reproduce full-suite performance, and environment seeds alone do not pin the model RNG stream.

Integrity

Each model.safetensors is 26,913,478,024 bytes, with these SHA256 digests:

Model	SHA256
Ego4WAM_VideoPretrained	c5ca7480e9fbf504704e2e5bc3d9aba0e415838ef50bc993213e810a18b99f05
Ego4WAM_VideoPretrained_RoboDojoJoint	ae1b4595e1909e4573ad3214e1c5112f1920a34675340f6adfd4f2e9ec6c12c4

Citation

@article{sun2026ego4wam,
  title={Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?},
  author={Sun, Zhihao and Liu, Liu and Wang, Xinjiang and Jiang, Haoyi and Feng, Wei and Zhang, Huiqiang and Jia, Xiaosong and Su, Zhizhong and Wu, Zuxuan},
  journal={arXiv preprint arXiv:2609.40341},
  year={2026}
}

License and attribution

The released weights and Ego4WAM-authored code additions/modifications are provided under Apache-2.0. Inherited StarVLA, FastWAM and third-party components retain their original licenses and copyright notices; see NOTICE and LICENSES in the source release. Wan2.2, UMT5, datasets, simulator SDKs and benchmark assets remain subject to their own terms. Each model directory contains its weights license.