A research paper titled "Learning Foresight without Explicit Trajectories for 3D Diffusion Policies" proposes that 3D diffusion policies, which generate geometrically grounded actions from current observations, need to anticipate where an interaction is heading rather than only knowing what motion is feasible now. The work states that existing policies largely leave such foresight to emerge implicitly.
3D diffusion policies are strong at generating geometrically grounded actions from current observations, but successful manipulation requires not only knowing what motion is feasible now, but also anticipating where the interaction is heading. Existing policies largely leave such foresight to emerge
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