SADP: Can Subgoal-Aware Diffusion Improve Long-Horizon Manipulation?
Can a diffusion policy learn both how to move and when to advance to the next manipulation stage? This episode examines SADP and the case for preserving subgoal structure in foundation-model-generated demonstrations. We cover three questions: how language-guided planning becomes executable training data, how action generation and continuation prediction work together, and what the comparison with TARAD reveals about explicit stage conditioning. The discussion connects SADP to VoxPoser's spatial planning, DP3's point-cloud control, and completion-aware approaches such as SeqVLA. We examine…
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