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Hourglass: Cross-Embodiment Motion Planning by Coupling a Body-Agnostic World Model with Differentiable Ego-Dynamics

The paper proposes the Hourglass architecture, which decomposes cross-embodiment motion planning into a body-agnostic world model and each robot's differentiable ego-dynamics template, coupled only through a camera ego-motion interface. On seven simulated robots across five motion types, this generalist policy achieves success rates 3.8–10.6 percentage points higher than imitation learning and diffusion policy baselines on the same world model, matches each robot's specialized policy without dedicated training, and comes within 1.4–2.8 percentage points of a parameter-conditioned MPPI teacher.

Part of the event: Hourglass proposes cross-embodiment motion planning architecture

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