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Real-world robots forget how to fold towels after learning to pick-and-place, but this work shows experience replay can help, if you do it right.
Extracting temporal geometry from generative models can boost reinforcement learning performance by over 2x without changing the optimal policy.
By explicitly modeling spatial interaction and manipulation intent, AIM achieves a 94% success rate on RoboTwin 2.0, suggesting that robot control can be drastically improved by explicitly reasoning about *where* to interact, not just *how* scenes evolve.
