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Robust out-of-distribution transfer is achieved by decoupling demonstration matching from policy learning, allowing robots to generalize effectively to unseen objects.
Achieving over 20% higher success rates in bimanual object reorientation tasks reveals the power of compositional diffusion combined with energy-based optimization.
