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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.
Robots learn better when they first imagine the future and then figure out how to act, unlocking SOTA performance by disentangling forward and inverse dynamics pretraining.
