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SPEAR: Selective Preference Elicitation with Adaptive Rewards for VLM-Guided Robotic Manipulation
Recent Vision-Language Models (VLMs) enable automated preference labeling for robotic manipulation, yet their deployment remains impractical since each query incurs substantial inference cost, while VLMs frequently abstain on visually challenging comparisons. We propose SPEAR, a framework that trans
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