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arXiv 2110.00534cs.CVcs.AIcs.CLcs.RO
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TEACh:任务驱动的具身智能体

TEACh: Task-driven Embodied Agents that Chat

Amazon Alexa AI(亚马逊 Alexa AI);University of Southern California(南加州大学);University of Michigan(密歇根大学)

Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava, Patrick Lange, Anjali Narayan-Chen, Spandana Gella, Robinson Piramuthu, Gokhan Tur, Dilek Hakkani-Tur

摘要:

在人类空间中运行的机器人必须能够与人进行自然语言交互,理解和执行指令,并使用对话来解决歧义并从错误中恢复。为了研究这一点,我们引入了TEACh,这是一个包含3,000多个人类交互对话的数据集,用于在模拟中完成家务任务。一个指挥官可以访问关于任务的预言信息,用自然语言与一个跟随者通信。跟随者在环境中导航并与环境交互,以完成从“煮咖啡”到“准备早餐”的复杂性不同的任务,询问问题并从指挥官那里获得更多信息。我们提出了三个基准使用TEACh来研究具身智能的挑战,我们评估初始模型的对话理解,语言基础和任务执行的能力。

英文摘要:

Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human--human, interactive dialogues to complete household tasks in simulation. A Commander with access to oracle information about a task communicates in natural language with a Follower. The Follower navigates through and interacts with the environment to complete tasks varying in complexity from "Make Coffee" to "Prepare Breakfast", asking questions and getting additional information from the Commander. We propose three benchmarks using TEACh to study embodied intelligence challenges, and we evaluate initial models' abilities in dialogue understanding, language grounding, and task execution.

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。

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