# Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data

arXiv: https://arxiv.org/abs/2608.02580 · HTML: https://mlsanity.com/paper/2608.02580/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2026.

## Built on by (most cited first, 11 of 11)

- [Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization](https://mlsanity.com/paper/2608.26103/) (2026)
- [MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence](https://mlsanity.com/paper/2609.25627/) (2026)
- [InternW0-Δ: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data](https://mlsanity.com/paper/2609.31394/) (2026)
- [In-Context Learning for Robots: Methods and Applications](https://mlsanity.com/paper/2609.36012/) (2026)
- [UniWAM: Unified World-Action Model](https://mlsanity.com/paper/2610.02054/) (2026)
- [From Seeing to Acting: Smart Glasses as First-Person Intelligence Platforms](https://mlsanity.com/paper/2608.24877/) (2026)
- [AtomEgo: Exploring Ego-Robot Integration for Embodied Foundation Model Pretraining](https://mlsanity.com/paper/2609.21461/) (2026)
- [Skel-WAM: A Hand-Skeleton-Conditioned World Action Model for Human-to-Robot Manipulation Transfer](https://mlsanity.com/paper/2609.21514/) (2026)
- [From World Models to World Action Models: Rethinking Next-State Prediction](https://mlsanity.com/paper/2609.34414/) (2026)
- [Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?](https://mlsanity.com/paper/2609.40341/) (2026)
- [Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand](https://mlsanity.com/paper/2610.20351/) (2026)

## Abstract (arXiv)

Learning generalizable robot manipulation policies requires large-scale and diverse demonstration data. Egocentric human manipulation videos offer rich scene and task diversity, and prior work has shown that retargeting and rendering such videos into robot-format data can yield effective per-task policies at small scale. However, whether this approach can provide pretraining benefits for vision-language-action models at scale remains unexplored. We present Ego2Robot, a scalable pipeline that converts egocentric human manipulation videos into robot training data through action retargeting, robot-arm visual synthesis, and multi-level quality curation. Ego2Robot supports both curated datasets and in-the-wild videos, producing 18,561 hours of robot training data spanning 15 robot morphologies, making it the largest ego-to-robot dataset to date. To evaluate generalization, we extend RoboTwin2.0 with disentangled perturbation axes covering visual appearance, scene layout, embodiment morphology, and task semantics. Experiments show that joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across multiple perturbation types, with benefits validated on real-robot deployment. Project page: https://www-ye.github.io/ego2robotblog/