Foundation models and other policies trained on large and diverse datasets can endow robots with semantic and task knowledge that would be difficult to acquire from task-specific demonstrations alone. Despite their broad scene understanding and reasoning capabilities, however, these models often lack the environment- and embodiment-specific knowledge required for precise and reliable execution. We propose GLOVES, a flow-based framework that complements an existing agent with a lightweight, goal-agnostic expert action prior that refines the action chunks proposed by the agent without updating its parameters. The prior captures observation-conditioned expert behavior, while the agent's proposal supplies task- and context-specific intent that the correction aims to preserve. The learned flow also provides a natural in-distribution scoring mechanism through reverse flow evaluation. We use this signal as an intervention gate: actions that appear consistent with the expert distribution are passed through unchanged, while anomalous or out-of-distribution (OOD) actions are corrected. In this way, GLOVES only provides assistance when necessary. By learning local goal-agnostic expert action patterns and using them to adapt agent-proposed actions during execution, GLOVES provides a lightweight shared-control module for robust action adaptation across tasks and environments. Across controlled OOD-recovery studies, simulation benchmarks, and real-robot experiments, GLOVES consistently improves the success of vision-language-action models (VLAs) and Code-as-Policy agents without retraining the underlying policy. Code and demos are available at ripl.github.io/GLOVES_web.

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