Abstract
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 transforms VLMs into efficient preference oracles via two synergistic mechanisms. First, we design distinguishability-aware query selection to reduce abstention rates by targeting only informative and distinguishable comparisons. Second, we deploy an adaptive reward transition that bootstraps exploration with VLM-synthesized guidance before preference-based refinement. Experiments on the manipulation benchmarks demonstrate that SPEAR achieves 88.9% average success, outperforming baselines by 23.3 percentage points, while significantly reducing abstention and wall-clock training time on contact-rich tasks. SPEAR learns three Mobile ALOHA manipulation tasks fully online on real hardware, demonstrating end-to-end real-world applicability. By addressing query efficiency and exploration challenges, SPEAR establishes VLMs as practical preference oracles for scalable robot learning without manual reward engineering.
Abstract (translated)
URL
https://arxiv.org/abs/2610.21621
