# SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus

arXiv: https://arxiv.org/abs/2411.12844 · HTML: https://mlsanity.com/paper/2411.12844/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 18 of 18)

- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Vision-and-Dialog Navigation](https://mlsanity.com/paper/1907.04957/) (2019)
- [Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments](https://mlsanity.com/paper/1811.12354/) (2018)
- [TEACh: Task-driven Embodied Agents that Chat](https://mlsanity.com/paper/2110.00534/) (2021)
- [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017)
- [Executing Instructions in Situated Collaborative Interactions](https://mlsanity.com/paper/1910.03655/) (2019)
- [Talk the Walk: Navigating New York City through Grounded Dialogue](https://mlsanity.com/paper/1807.03367/) (2018)
- [A Review of Verbal and Non-Verbal Human-Robot Interactive Communication](https://mlsanity.com/paper/1401.4994/) (2014)
- [How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus](https://mlsanity.com/paper/2106.06504/) (2021)
- [A Pipeline for Creative Visual Storytelling](https://mlsanity.com/paper/1807.08077/) (2018)
- [Envisioning Narrative Intelligence: A Creative Visual Storytelling Anthology](https://mlsanity.com/paper/2310.04529/) (2023)
- [Applying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue](https://mlsanity.com/paper/1703.03714/) (2017)
- [Consequences and Factors of Stylistic Differences in Human-Robot Dialogue](https://mlsanity.com/paper/1807.08076/) (2018)
- [Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue](https://mlsanity.com/paper/1710.06406/) (2017)
- [A Research Platform for Multi-Robot Dialogue with Humans](https://mlsanity.com/paper/1910.05624/) (2019)
- [Balancing Efficiency and Coverage in Human-Robot Dialogue Collection](https://mlsanity.com/paper/1810.02017/) (2018)

## Built on by (most cited first, 4 of 4)

- [Limited Linguistic Diversity in Embodied AI Datasets](https://mlsanity.com/paper/2601.03136/) (2026)
- [AHA -- Predicting What Matters Next: Online Highlight Detection Without Looking Ahead](https://mlsanity.com/paper/2509.16421/) (2025)
- [What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics](https://mlsanity.com/paper/2510.16435/) (2025)
- [When Should a Failing Robot Ask? Initiating Corrective Human-Robot Dialogue from Audited Sensor Evidence](https://mlsanity.com/paper/2609.21942/) (2026)

## Abstract (arXiv)

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker's intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered.