Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models
We introduce Action--Future Coupled Effect Learning (AFCE), which makes a future-grounded physical Effect the policy's primary prediction target.
ProofPaper ↗
Key points
- 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.
- An offline tokenizer constructs temporally ordered Effect tokens from demonstrated actions, realized robot responses, and sparse visual transitions.
- A flow-matching VLA predicts Effects from current images, instruction, and robot state, while a state-conditioned decoder converts them into action chunks.
Sources (1)
- [1]Beyond Actions: Learning Future Operation Targets for Vision-Language-Action ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 08:29 AM
We introduce Action--Future Coupled Effect Learning (AFCE), which makes a future-grounded physical Effect the policy's primary prediction target.
Actions specify what a robot should execute, but do not describe what those commands will achieve.
Extractive summary: sentences quoted from the sources.
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