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This work extends planning under a Riemannian metric from unconstrained spaces to constraint submanifolds by applying the induced metric in both a sampling-based planner and a trajectory optimizer.
The intrinsic dimensions learned by diffusion models align perfectly with the analytical degrees of freedom of constraint manifolds, revealing a surprising depth of geometric understanding in these models.
