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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 RLBench simulations and physical UR5e manipulation, emphasizing the missing independent, closed-loop validation of stage reporting. For roboticists building imitation-learning pipelines, the practical question is what to retain when converting a planner's execution into a dataset. Should intermediate instructions and transition events be treated as disposable bookkeeping, or as valuable supervision? This episode separates the contribution in data organization from the architecture around it, and explains why a readable execution stage could become an important interface between learned control and robot supervision. What changes when a demonstration teaches the procedure as well as the motion?
