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Experiments on a real robot platform demonstrate that MaskHarness-WAM substantially outperforms limited-horizon policies on sequential multi-object manipulation, showing its effectiveness in extending local manipulation skills to reliable long-horizon execution.
DSWAM bridges the gap between coarse user commands and fine-grained robot actions, outperforming traditional models in real-world task execution.
Injecting 4D geometric priors into World Action Models boosts robotic manipulation performance without adding inference overhead.
