EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations

[2606.12604] EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations

[Submitted on 10 Jun 2026]

Title:EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations

View a PDF of the paper titled EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations, by Yangcen Liu and 8 other authors

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Abstract:Dexterous manipulation is limited by the cost of collecting large-scale robot demonstrations. Egocentric human videos offer a scalable source of diverse manipulation behaviors, but directly using them for robot learning requires bridging two gaps: the visual gap between human and robot observations, and the action gap between human motion and robot-executable action. We propose EgoEngine, a scalable framework for transforming egocentric human manipulation videos into high-fidelity robot data. Given an egocentric RGB video, EgoEngine produces: (i) a high-fidelity robot observation video replacing human with robot while preserving scene context and temporal alignment, and (ii) a task-aligned, executable robot action trajectory under feasibility constraints. Experiments in simulation and on real robots show that EgoEngine enables scalable conversion of human videos into robot data and, to our knowledge, demonstrates the first zero-shot visuomotor dexterous policy learning from egocentric human videos without real-robot demonstrations. Project website: this https URL.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2606.12604 [cs.RO]

(or arXiv:2606.12604v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.12604

arXiv-issued DOI via DataCite

Submission history

From: Yangcen Liu [view email] [v1] Wed, 10 Jun 2026 19:01:40 UTC (40,957 KB)

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