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CereVLA: Cerebellum-Inspired Consequence-Aware Residual Governance for Efficient Vision-Language-Action Execution

Zeng, Shuai et al.
arXiv (All) · Papers
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computer-vision-and-pattern-recognitioni-2-9systems-and-control
Action-chunked vision-language-action (VLA) policies improve inference efficiency, but limited feedback within committed action chunks can lead to accumulated execution errors. Residual adaptation can correct such deviations without retraining the VLA; however, existing corrections are typically opt
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