English(EN) JRDB-AVR: An Active Visual Reasoning Benchmark for Embodied Agents in Real-World Environments

新基准评估具身智能体的积极视觉推理能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员推出了JRDB-AVR，这是一个旨在评估具身智能体积极视觉推理能力的新基准。与以往侧重于被动观察的基准不同，JRDB-AVR要求智能体通过请求不同视角和时间下的特定观察来积极寻找证据。该基准源自真实世界的机器人数据，旨在通过评估最终答案和所依据的视觉证据来解决智能体提供看似合理但缺乏支持的答案的问题。实验表明，当前模型在答案准确性和证据准确性之间存在显著差距，凸显了在具身视觉推理中进行积极的、证据感知的评估的必要性。 AI

影响该基准有望推动更强大的具身AI智能体的发展，使其能够进行复杂的推理和证据收集。

排序理由该集群描述了一个用于评估AI智能体的新基准，该基准发表在arXiv论文中。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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arXiv cs.AI TIER_1 English(EN) · Zhixi Cai, Fucai Ke, Sukai Huang, Maria Garcia de la Banda, Peter J. Stuckey, Gholamreza Haffari, Hamid Rezatofighi · 2026-10-27 04:00

JRDB-AVR：面向真实环境中具身智能体的积极视觉推理基准

arXiv:2609.35032v2 Announce Type: replace Abstract: In complex embodied visual reasoning scenarios, an agent often has only a limited field of view, and the evidence needed to answer a question may be distributed across time, viewpoint, and interacting objects. A model may theref…

本文如何被排名

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JRDB-AVR：面向真实环境中具身智能体的积极视觉推理基准

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