AtomEgo: Exploring Ego-Robot Integration for Embodied Foundation Model Pretraining

Abstract:Embodied foundation models are constrained by the limited scale and diversity of robot demonstrations, motivating the use of large-scale egocentric human interaction data. However, how to effectively incorporate such data into embodied-model pre-training remains unclear because of substantial embodiment and action-space gaps between humans and robots. We present AtomEgo, a systematic study of ego--robot co-training supported by a curated corpus of approximately 2,659 hours and a scalable data processing pipeline. Across vision--language--action and world--action model architectures, we investigate three representative paradigms: joint co-training with domain-specific action heads, progressive ego-to-robot transfer through embodiment alignment, and joint video--action modeling. We evaluate these paradigms through multi-task real-robot experiments and language-conditioned cross-embodiment representation analysis. Our results reveal a simple principle: Data Scale * Alignment Quality --> Capability Gain; egocentric data can improve generalization, but their value depends on how effectively they are aligned and utilized. This principle can provide practical guidance for scalable ego--robot pre-training.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2609.21461 [cs.RO]

(or arXiv:2609.21461v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.21461 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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