arXiv — cs.AI preprintsInternational12 October 2026
Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models
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arXiv:2610.13212v1 Announce Type: cross Abstract: Actions specify what a robot should execute, but do not describe what those commands will achieve. Vision-language-action (VLA) policies nevertheless learn primarily by predicting action chunks from images, language, and state. We introduce Action--Future Coupled Effect Learning (AFCE), which makes a future-grounded physical Effect the policy's primary prediction target. An offline tokenizer constructs temporally ordered Effect tokens from demonstrated actions, realized robot responses, and sparse visual transitions. Reconstruction objectives e
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