EfficientEQA：一种面向开放词汇具身问答的高效方法
EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering
AI总结：
提出EfficientEQA框架，通过语义价值加权前沿探索、BLIP自适应停止机制和检索增强生成，实现开放词汇具身问答的高效探索与准确作答，准确率提升超15%，探索步骤减少超20%。
摘要：
具身问答（EQA）对机器人助手来说是一项至关重要且具挑战性的任务。大型视觉语言模型（VLMs）在EQA方面展现了潜力，但现有方法要么将其视为缺乏主动探索的静态视频问答，要么将答案限制在封闭的选择集中。这些限制阻碍了现实世界的适用性，因为在现实世界中，机器人必须高效探索并在开放词汇设置下提供准确答案。为克服这些挑战，我们引入了EfficientEQA，这是一个将高效探索与自由形式答案生成相结合的新颖框架。EfficientEQA具有三项关键创新：（1）语义价值加权前沿探索（SFE），利用黑盒VLM的言语化置信度（VC）来优先探索语义重要的区域，使智能体能更快收集相关信息；（2）基于BLIP相关性的机制，通过将高度相关的观测标记为异常值来指示智能体是否已收集足够信息，从而自适应地停止；（3）检索增强生成（RAG）方法，使VLM基于智能体观测历史中的相关图像准确作答，而无需依赖预定义选项。实验结果表明，EfficientEQA的答案准确率比现有最优方法高15%以上，且所需探索步骤减少20%以上。
英文摘要：
Embodied Question Answering (EQA) is an essential yet challenging task for robot assistants. Large vision-language models (VLMs) have shown promise for EQA, but existing approaches either treat it as static video question answering without active exploration or restrict answers to a closed set of choices. These limitations hinder real-world applicability, where a robot must explore efficiently and provide accurate answers in open-vocabulary settings. To overcome these challenges, we introduce EfficientEQA, a novel framework that couples efficient exploration with free-form answer generation. EfficientEQA features three key innovations: (1) Semantic-Value-Weighted Frontier Exploration (SFE) with Verbalized Confidence (VC) from a black-box VLM to prioritize semantically important areas to explore, enabling the agent to gather relevant information faster; (2) a BLIP relevancy-based mechanism to stop adaptively by flagging highly relevant observations as outliers to indicate whether the agent has collected enough information; and (3) a Retrieval-Augmented Generation (RAG) method for the VLM to answer accurately based on pertinent images from the agent's observation history without relying on predefined choices. Our experimental results show that EfficientEQA achieves over 15% higher answer accuracy and requires over 20% fewer exploration steps than state-of-the-art methods. Our code is available at: https://github.com/chengkaiAcademyCity/EfficientEQA
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
