---
license: apache-2.0
task_categories:
- robotics
- question-answering
tags:
- Navigation
- Retrieval
- Visual Embedding
- Multimodal
- VLM
- MLLM
size_categories:
- n<1K
---


# RAVEN: Long-Horizon Reasoning and Navigation with a Visuo-Spatial-Temporal Memory

[Website](https://#) [Code](https://#) [Citation](#citation)


Introduction:
We release the RAVEN-QA dataset. 

- Task: given a subsampled list of frames from a long video, each paired with timestamps and positions, and given a user query, 
the model or robot will find the time or positions where the queried thing shows up. The query can be objects, places, events, and concepts. 

- Categories: They cover dominant and secondary object retrieval (dominant or secondary in view), visual reasoning, commonsense reasoning, information recall, and spatial understanding. 

- Diversity: The dataset covers real-world and simulation robot view videos, web-sourced human view videos, and our self-recorded tour videos.

## Parts

1. irs: A simple retrieval dataset, including YouTube indoor and outdoor videos.
   (6 videos; 54 queries; with text and image queries)
2. irs_hard: A harder human-ego retrieval dataset, including self-recorded and web-sourced videos for more challenging object-finding.
   (3 videos; 41 queries; with text queries)
3. habitat_sim: A robot simulation dataset in Habitat environments.
   (19 videos; 157 queris; with text queries)
4. real_world: A real-world robot exploration dataset taken in our labs and public areas.
   (4 videos; 21 queries; with text queries)





## File Structure

```
dataset_name/
├─ subsplit_name/
│  ├─ video_name/
│  │  ├─ frames/
│  │  │  ├─ frame_001.jpg
│  │  │  ├─ frame_002.jpg
│  │  │  └─ ...
│  │  └─ questions.json
│  ├─ ...
│  │
├─ subsplit_name/
│  ├─ video_name/

```



## Citation

TODO