The first part develops Humanoid Policy ~ Human Policy for cross-embodiment supervision in humanoid manipulation. It places humans and humanoids in a unified state-action representation, enabling a transformer policy to co-train on human and robot demonstrations and retarget its predictions at deployment. To support this formulation, we introduce PhD^2, a task-oriented egocentric human demonstration dataset that expands data scale without discarding embodiment structure.
The second part presents EmbodyHOI, which addresses a harder embodiment-gap setting in dexterous hand-object interaction. It starts from a flow-matching diffusion model trained in human hand-object space, then applies a differentiable guidance function during sampling to steer trajectories toward a target humanoid embodiment, jointly optimizing wrist reachability and base placement before downstream control.

Together, these chapters show that scalable robot manipulation requires data transformations that preserve task structure while respecting the robot body.