Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models
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 encourage the representation to retain executable detail and observed physical change. A flow-matching VLA predicts Effects from current images, ins...
| Authors | Min Lin, Bokai Zhou, Jianqi Lin et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 October 2026 |
| Last updated at source | 12 October 2026 |
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