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Task-conditioned wrist modeling boosts robot manipulation accuracy, revealing how wrist interactions can be anticipated for better performance.
SG-WAM achieves unprecedented accuracy in action-conditioned future state prediction by integrating geometry-aware dynamics directly into the policy representation space.
By compressing future observations into a learned "condition space," WoG enables VLA models to generate more precise actions and generalize better than models relying on direct future prediction.
