Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents

مقتطف من المصدر

تنظم هذه الصفحة محتوى عن الذكاء الاصطناعي والبحث لتسهيل قراءته والاستشهاد به والتحقق منه من قبل الباحثين والطلاب وأدوات الذكاء الاصطناعي.

arXiv:2605.12620v1 Announce Type: new Abstract: Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in AI. Multimodal Large Language Models (MLLMs) have significantly advanced the reasoning capabilities of such agents through strong vision-language knowledge and chain-of-thought (CoT) reasoning, yet remain brittle when faced with challenging out-of-distribution scenarios. To address this, we propose Verifier-Guided Action Selection (VegAS), a test-time framework designed to improve the robustness of MLLM-based embodied agents through an explicit verification step. At inference time, rather than committing to a single decoded action, VeGAS samples an ensemble of candidate actions and uses a generative verifier to identify the most reliable choice, without modifying the underlying policy. Crucially, we find that using an MLLM off-the-shelf as a verifier yields no improvement, motivating our LLM-driven data synthesis strategy, which automatically constructs a diverse curriculum of failure cases to expose the verifier to a rich distribution of potential errors at training time. Across embodied reasoning benchmarks spanning the Habitat and ALFRED environments, VeGAS consistently improves generalization, achieving up to a 36% relative performance gain over strong CoT baselines on the most challenging multi-object, long-horizon tasks.

عرض المصدر الأصلي

المصدر الرسمي أو الأصلي: arXiv AI. تحقق دائما من التفاصيل في المصدر الأساسي.

العودة إلى الذكاء الاصطناعي والبحث