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.

引入 TEACh 数据集，通过三个基准测试，研究基于人类空间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，使用会话来解决模糊性并从错误中恢复的具体挑战。