EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation

Egocentric human demonstrations offer an accessible source of task experience, but differences in body scale and controller response, together with missing robot states, limit their value as humanoid training supervision. We present EgoAlign, a data-construction framework that converts these demonstrations into action and state supervision compatible with a general-purpose, continuous whole-body controller, without collecting physical-robot demonstrations.

From the abstract. Our summary is in progress.