aiqbench analysis
EgoEngine addresses the critical bottleneck in robot learning by converting abundant egocentric human videos into executable robotic demonstrations without requiring direct real-robot interaction for every task. This framework bridges visual and action gaps to enable zero-shot visuomotor policy learning, significantly lowering data collection costs for dexterous manipulation tasks.
EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations
arXiv:2606.12604v1 Announce Type: new 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: https://egoengine.github.io.
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.
Robotics (cs.RO)
arXiv:2606.12604 [cs.RO]
(or arXiv:2606.12604v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.12604 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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