论文标题
拨号：以对话为基础的代理，用于体现指令以下
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
论文作者
论文摘要
语言指导的体现了AI基准，要求代理浏览环境并操纵对象通常允许单向通信：人类用户向代理提供了自然语言命令，而代理只能被动地遵循命令。我们介绍了基于Alfred基准的基准测试后的拨号式拨号。 Dialfred允许代理商积极向人类用户提出问题；代理使用用户响应中的其他信息来更好地完成其任务。我们发布了一个具有53K任务的问题和答案的人类注销的数据集，以及一个回答问题的甲骨文。为了解决拨号框，我们提出了一个提问者绩效框架，其中提问者通过人类通知的数据进行了预训练，并通过增强学习进行了微调。我们可以公开拨号，并鼓励研究人员提出和评估他们的解决方案，以构建支持对话的体现代理。
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.
