Firehose

17 SEP 2026 · Paper

Learning Foresight without Explicit Trajectories for 3D Diffusion Policies

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.

3D diffusion policies action learning interaction forecasting robotics manipulation tasks

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Learning Foresight without Explicit Trajectories for 3D Diffusion Policies