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Ego-CEVR: Learning Cross-Embodiment Visual Representations from Human Egocentric Videos

Ego-CEVR proposes a data-native hierarchical visual representation learning framework that assigns distinct learning objectives to global and interaction representations. The global module learns object relations, task state, and temporal progress through future prediction on egocentric video; the interaction module interprets wrist observations in global context and learns local alignment and contact cues through robot action supervision. Two residual paths feed both representations into the action policy simultaneously, and the global video module can be pretrained independently on large-scale human egocentric videos and videos from multiple robot embodiments, without robot action labels or paired wrist observations.

Part of the event: Ego-CEVR proposes cross-embodiment visual representation learning framework

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