ELBA: 面向具身视觉导航与任务完成的通过提问学习方法
ELBA: Learning by Asking for Embodied Visual Navigation and Task Completion
AI总结：
针对现有具身智能体缺乏主动提问消解环境歧义能力的问题，提出ELBA模型学习提问时机与内容以获取任务补充信息，在TEACh数据集上表现优于无问答能力的基线模型。
摘要：
研究界对设计可辅助人类完成任务的智能具身智能体的兴趣日益浓厚。尽管相关视觉语言基准已取得显著进展，但以往大多数研究都聚焦于构建遵循指令的智能体，而非赋予智能体提问能力，以主动解决具身环境中自然出现的歧义问题。为填补这一空白，我们提出了ELBA（具身提问学习）模型，该模型可学习何时提问、提出何种问题，从而动态获取完成任务所需的额外信息。我们在TEACh视觉-对话导航与任务完成数据集上对ELBA进行了评估。实验结果表明，与不具备问答能力的基线模型相比，所提方法取得了更优的任务性能。
英文摘要：
The research community has shown increasing interest in designing intelligent embodied agents that can assist humans in accomplishing tasks. Although there have been significant advancements in related vision-language benchmarks, most prior work has focused on building agents that follow instructions rather than endowing agents the ability to ask questions to actively resolve ambiguities arising naturally in embodied environments. To address this gap, we propose an Embodied Learning-By-Asking (ELBA) model that learns when and what questions to ask to dynamically acquire additional information for completing the task. We evaluate ELBA on the TEACh vision-dialog navigation and task completion dataset. Experimental results show that the proposed method achieves improved task performance compared to baseline models without question-answering capabilities.
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