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Shanghai AI Laboratory, The University of Hong Kong
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Forget short-term context windows: VPWEM's Transformer-based memory compressor lets robots ace long-horizon manipulation tasks by distilling past observations into fixed-size episodic memories.
Bimanual robots can now achieve robust dexterous grasping in the real world, thanks to a massive 20M-frame synthetic dataset and a simple attention-based policy that transfers surprisingly well.
