Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively. We present DialFRED, a dialogue-enabled embodied instruction following benchmark based on the ALFRED benchmark. DialFRED allows an agent to actively ask questions to the human user; the additional information in the user's response is used by the agent to better complete its task. We release a human-annotated dataset with 53K task-relevant questions and answers and an oracle to answer questions. To solve DialFRED, we propose a questioner-performer framework wherein the questioner is pre-trained with the human-annotated data and fine-tuned with reinforcement learning. We make DialFRED publicly available and encourage researchers to propose and evaluate their solutions to building dialog-enabled embodied agents.

该研究提出了DialFRED，一个基于ALFRED基准的对话启用的具有体现指导AI的基准，其允许代理主动向人类用户提问，并利用用户响应中的附加信息来更好地完成其任务。使用人类注释的数据集和强化学习调整预先训练的提问者来解决DialFRED，并且鼓励研究人员提出和评估构建具有对话功能的体现代理的解决方案。

DialFRED: 嵌入式指令跟随的对话驱动代理