arXiv Computer Vision September 24, 2026 By Shuai Zeng, Yuxuan Liang, Hangmiao Hu, Fobao Zhou, Zixiang Wang, Wenxi Hong, Hang Zhao

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

Read the original on arXiv Computer Vision →

CereVLA is a cerebellum-inspired framework that enhances vision‑language‑action (VLA) policies by adding lightweight residual refinement and predictive consequence evaluation to frozen VLA execution. It generates corrective actions via flow‑based residual refinement, then assesses their short‑ and interval‑horizon impacts using a recurrent state‑space model and a history‑aware classifier, suppressing unfavorable corrections with a lightweight governor. Experiments on LIBERO‑10, LIBERO‑GOAL, and SO‑101 show that CereVLA improves task success rates and reduces control steps compared to state‑of‑the‑art baselines.

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