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EgoSteer-3B-RealMan is an open-weight model for robotics from EgoSteer, released under Apache License 2.0. Its published files total 5.6 GB. It draws 25 downloads a month.
EgoSteer-3B-RealMan is the generalist policy from EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos.
By EgoSteer, published under apache-2.0, revision e4566133de8c.
EgoSteer-3B-RealMan is the generalist policy from EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos. It is a world-model-enhanced Vision-Language-Action (VLA) policy built on a Qwen3-VL backbone with a flow-matching action expert, post-trained from EgoSteer-3B-Base on real-world demonstrations collected on the RealMan robot. This is a robot-ready dual-camera (head + chest) checkpoint, corresponding to EgoSteer-9.6K-DG in the paper. For the base model to fine-tune on your own data, see EgoSteer-3B-Base. Our full-stack system integrates EgoSmith (data pipeline), Robot Stack (deployment), and EgoSteer (policy) to learn from 9.6k hours of large-scale…
EgoSteer-3B-RealMan is the generalist policy from EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos. It is a world-model-enhanced Vision-Language-Action (VLA) policy built on a Qwen3-VL backbone with a flow-matching action expert, post-trained from EgoSteer-3B-Base on real-world demonstrations collected on the RealMan robot.
This is a robot-ready dual-camera (head + chest) checkpoint, corresponding to EgoSteer-9.6K-DG in the paper. For the base model to fine-tune on your own data, see EgoSteer-3B-Base.
Our full-stack system integrates EgoSmith (data pipeline), Robot Stack (deployment), and EgoSteer (policy) to learn from 9.6k hours of large-scale egocentric human videos and facilitate data-efficient real-robot post-training, enabling steerable dexterous manipulation across over 40 tasks alongside few-shot adaptation to complex, long-horizon tasks. EgoSteer-3B-RealMan runs on the RealMan embodiment out of the box and can be further post-trained for other embodiments.
| Component | Description |
|---|---|
| Backbone | Qwen3-VL-2B-Instruct |
| Action expert | Flow-matching (DiT / AdaLN) expert reusing the backbone KV prefix |
| World model expert | Regresses future-frame features from a frozen DINOv3 ViT-L/16 teacher, training only |
| Action space | Unified human-to-robot space based on wrist poses and fingertip keypoints |
| Cameras | Dual-camera: head + chest |
| Total parameters | ~3B |
The policy maps a language instruction plus multi-view RGB and proprioception to a chunk of
future actions. Keep these consistent with the bundled config.yaml and normalizer.pkl.
| Specification | |
|---|---|
| Instruction | A natural-language task description |
| Cameras | Dual RGB — head + chest, 480 × 640, 6-frame history (stride 30 over a 30 fps base) |
| Intrinsics | Per-camera [fx, fy, cx, cy] (head + chest) — required. By default rendered into the VLM prompt as text (camera_intrinsic_mode: text); rescaled to match the resized image |
| Proprioception (state) | 48-D: bimanual wrist poses (2 × [3 translation + 6D rotation] = 18), expressed in the camera frame + fingertip keypoints (2 hands × 5 fingertips × 3D = 30), expressed in wrist frame, 6-frame history |
| Action output | 48-D relative action in the same layout as the state, and relative to the current state, predicted as a 32-step action chunk |
| Normalization | State/action normalized with the bundled normalizer.pkl (relative action space) — required for inference and fine-tuning |
| Model | Parameters | Description |
|---|---|---|
| EgoSteer-3B-Base | 3B | Base EgoSteer model trained on 9.6k hours of egocentric human videos, ready for fine-tuning |
| EgoSteer-3B-RealMan (this repo) | 3B | Generalist post-trained on real-world data collected on the RealMan robot |
| File | Description |
|---|---|
model_bf16.pt |
bf16 model weights |
config.yaml |
RealMan post-training config, for rebuilding the network at eval and inference. Further post-training uses your own config, weights only |
normalizer-relative-10k-pretrain/normalizer.pkl |
State/action normalizer — required for fine-tuning and inference (shared with EgoSteer-3B-Base; relative action space) |
This is a custom policy, loaded and run with the EgoSteer codebase.
This is a dual-camera policy, so a few config keys differ from the single-camera base.
When serving or evaluating, keep these consistent with the bundled config.yaml:
data.target_image_size: [480, 640]dataset.vla_dataset.load_chest: true (head + chest)data.max_vlm_tokens: 2176 — dual-camera uses roughly twice the vision tokens of
single-camera; a smaller value truncates the input and fails with a video-token-count
mismatch.policy.rtc_config.enabled: true — real-time chunking (RTC) for smooth asynchronous
closed-loop control; tolerates up to max_delay: 6 steps of inference latency. Disable it
for synchronous, blocking action-chunk execution.EgoSteer depends on two pretrained backbones; download them ahead of time:
EgoSteer-3B-RealMan is post-trained from EgoSteer-3B-Base (pretrained on 9.6k hours of egocentric human videos in a unified human-to-robot action space) on real-world dual-camera demonstrations collected on the RealMan robot. See the paper and project page for details.
@misc{zhong2026egosteerfullstacksteerabledexterous,
title={EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos},
author={Yifan Zhong and Zhang Chen and Tianrui Guan and Fanlian Zeng and Yuyao Ye and Tianjia He and Ka Nam Lui and Jiayi Li and Tingrui Zhang and Ruilin Yan and Xinhao Ji and Guangyu Zhao and Wenjie Lou and Jiayuan Zhang and Yuanpei Chen and Yaodong Yang},
year={2026},
eprint={2607.09701},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2607.09701},
}
Released under the Apache 2.0 license.
5 files, 5.6 GB in total. The weights are 1 file totalling 5.6 GB in pt.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model_bf16.pt | Weights | 5.6 GB | 22a244597ccd |
| config.yaml | Configuration | 8.0 KB | — |
| README.md | Documentation | 7.3 KB | — |
| normalizer-relative-10k-pretrain/normalizer.pkl | Other | 10.2 KB | bcae99466048 |
| .gitattributes | Repository | 1.5 KB | — |
Released by EgoSteer through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 5.6 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. EgoSteer-3B-RealMan is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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EgoSteer-3B-Base is the base world-model-enhanced Vision-Language-Action (VLA) policy from EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos. It is built on a Qwen3-VL backbone with a flow-matching action expert and a DINOv3 latent future-prediction objective used only during training, and learns a unified human-to-robot action space from large-scale egocentric human videos. This is the pretrained base checkpoint, corresponding to EgoSteer-9.6K-PT in the paper. For a robot-ready generalist, see Our full-stack system integrates EgoSmith (data pipeline), Robot Stack (deployment), and EgoSteer (policy) to learn from 9.6k hours of large-scale…