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The paper uses vision-language models to label pairwise preferences for robot reward learning, with the goal of cutting query cost and abstention. Its strongest evidence is the three online tasks on Mobile ALOHA hardware, but the 88.9% success and 23.3-point gain are reported only on simulated benchmarks and depend on baseline choices that readers should check. For ML practitioners, the useful idea is treating a VLM as a noisy, costly oracle and filtering queries for distinguishability before spending budget on them.
SPEAR: Selective Preference Elicitation with Adaptive Rewards for VLM-Guided Robotic Manipulation
arXiv:2610.21621v1 Announce Type: new 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: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.
8 pages, 4 figures. Accepted at IROS 2026. Project page: this https URL
Robotics (cs.RO)
arXiv:2610.21621 [cs.RO]
(or arXiv:2610.21621v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.21621 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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