TEACh
Agents complete household tasks in simulated homes, such as making coffee or preparing breakfast, guided by a dialogue between a Commander who knows the task and a Follower who acts. The dataset holds over 3,000 human dialogues and defines three benchmarks: acting from dialogue history, predicting the trajectory from a dialogue, and completing the task as two agents. Scores are task success rates.
It runs in the AI2-THOR simulator. About 150 GitHub stars, and the repo was last pushed in 2024-05.
- Made by
- Amazon Alexa AI
- Released
- October 2021
- Kind
- Simulated robots
- Level
- Everyday
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
In the authors’ words
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.
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for TEACh yet.
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
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