TEACh: Task-driven Embodied Agents that Chat
Not on the map. Why?
Cited by 32 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 194 papers in the map (data through 7 Oct 2026). Builds on 26 of its 49 references that are in the map. First posted on arXiv 1 Oct 2021.
Builds on
26 of 49 references.
References · 26
- 2017Mask R-CNNcited by 7,259
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- 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
- 2018VirtualHome: Simulating Household Activities via Programscited by 411
- 2018Speaker-Follower Models for Vision-and-Language Navigationcited by 337
- 2019Vision-and-Dialog Navigationcited by 246
- 2020Experience Grounds Languagecited by 241
- 2018Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environmentscited by 238
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- 2018Mapping Instructions to Actions in 3D Environments with Visual Goal Predictioncited by 101
- 2019Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learningcited by 100
14 more
- 2021A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Executioncited by 91
- 2019Executing Instructions in Situated Collaborative Interactionscited by 68
- 2021Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completioncited by 57
- 2019Just Ask:An Interactive Learning Framework for Vision and Language Navigationcited by 53
- 2021Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoringcited by 53
- 2015Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequencescited by 50
- 2020BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Stepscited by 49
- 2018Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Predictioncited by 35
- 2020RMM: A Recursive Mental Model for Dialog Navigationcited by 32
- 2018Shifting the Baseline: Single Modality Performance on Visual Navigation & QAcited by 30
- 2021The MineRL BASALT Competition on Learning from Human Feedbackcited by 24
- 2017Learning Interpretable Spatial Operations in a Rich 3D Blocks Worldcited by 15
- 2020ArraMon: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environmentscited by 14
- 2021VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigatorcited by 13
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Citing papers per year: 2021: 2; 2022: 26; 2023: 45; 2024: 41; 2025: 42; 2026: 38.
194 papers.
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