This paper develops a method to help 3D diffusion policies anticipate the future trajectory of an interaction, allowing them to generate more effective actions. Practitioners caring about robotics or manipulation tasks may benefit from this approach, as it can lead to improved performance in tasks like picking and placing objects.
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