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Can a robot learn manipulation more reliably when training teaches it what should change in the world? This episode explains how VLA-Delta turns physical state transitions into supervision and examines what its experiments establish. We cover three connected questions: how difference text, structured slots, and optical flow shape a shared representation; why rich training targets can support an action-focused deployment path; and what the ablations reveal beyond additional action-only pretraining. The discussion spans LIBERO simulation, bimanual RoboTwin tasks, and real manipulation on an AgileX PiPER dual-arm platform, including the reported average advantage over π0.5. We also connect the design to ZR-0, WorldVLA, and VLA-JEPA, showing how different choices of intermediate targets organize robot learning. The central limitation is experimental: higher task success does not establish that the policy understands action-dependent physical causality or recovers better when an expected transition fails. For roboticists working on imitation learning and vision-language-action models, the episode offers a practical way to think about supervision design. What useful physical event is already visible in your demonstrations but missing from your loss?
