EgoWAM: Bridging the Human-Robot Embodiment Gap via World Representation
danfei_xu · x · 2026-07-09
The research proposes the EgoWAM method to address the performance degradation caused by directly co-training robot policies with in-the-wild human data. Because humans and robots move very differently, EgoWAM bridges this embodiment gap through motion-centric world representations and strong visual pre-training, enabling both to achieve similar results despite their distinct motions.
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