Datasets:
File size: 4,778 Bytes
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---
dataset_info:
features:
- name: ep_id
dtype: string
- name: video
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: task_id
dtype: string
- name: high_level_category
dtype: string
- name: low_level_category
dtype: string
- name: num_interactions
dtype: int64
splits:
- name: train
num_bytes: 107506980
num_examples: 79213
- name: validation
num_bytes: 9653447
num_examples: 5870
download_size: 14758637
dataset_size: 117160427
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- robotics
- embodied-ai
pretty_name: findingdory
size_categories:
- 10K<n<100K
---
<center>
<a href="https://arxiv.org/abs/2506.15635" target="_blank">
<img alt="arXiv" src="https://img.shields.io/badge/arXiv-FindingDory-red?logo=arxiv" height="20" />
</a>
<a href="https://findingdory-benchmark.github.io/" target="_blank">
<img alt="Website" src="https://img.shields.io/badge/🌎_Website-FindingDory-blue.svg" height="20" />
</a>
<a href="https://github.com/findingdory-benchmark/findingdory-trl" target="_blank">
<img alt="GitHub Code" src="https://img.shields.io/badge/Code-FindingDory--TRL-white?&logo=github&logoColor=white" />
</a>
<a href="https://huggingface.co/yali30/findingdory-qwen2.5-VL-3B-finetuned" target="_blank"">
<img alt="Huggingface Model" src="https://img.shields.io/badge/Model-FindingDory-yellow?logo=huggingface" />
</a>
</center>
<center><h1>FindingDory: A Benchmark to Evaluate Memory in Embodied Agents</h1>
<a href="https://www.karmeshyadav.com/">Karmesh Yadav*</a>,
<a href="https://yusufali98.github.io/">Yusuf Ali*</a>,
<a href="https://gunshigupta.netlify.app/">Gunshi Gupta</a>,
<a href="https://www.cs.ox.ac.uk/people/yarin.gal/website/">Yarin Gal</a>,
<a href="https://faculty.cc.gatech.edu/~zk15/">Zsolt Kira</a>
</center>
Current vision-language models (VLMs) struggle with long-term memory in embodied tasks. To address this, we introduce **FindingDory**, a benchmark in Habitat that evaluates memory-based reasoning across 60 long-horizon tasks.
In this repo, we release the FindingDory Video Dataset. Each video contains images collected from a robot’s egocentric view as it navigates realistic indoor environments and interacts with objects. This dataset was used to train and evaluate the high-level agent SFT agent in the FindingDory benchmark.
# Usage
```
from datasets import load_dataset
dataset = load_dataset("yali30/findingdory")
```
# Dataset Structure
| Field name | Description |
| ------------------------- | ------------------------------------------------------------------------------------------------------------- |
| **ep\_id** | Episode id. |
| **video** | Relative path of the video clip. |
| **question** | Question posed to the agent based on the episode. |
| **answer** | Ground-truth answer stored as a list of image indices |
| **task\_id** | Identifier indicating which task template the episode belongs to (string). |
| **high\_level\_category** | Higl-task task category label. (Options: Single-Goal Spatial Tasks, Single-Goal Temporal Tasks, Multi-Goal Tasks). |
| **low\_level\_category** | Fine-grained task category label. (Example categories: Interaction-Order, Room Visitation, etc) |
| **num\_interactions** | Number of objects the robot interacts with, during the experience collection. |
Notes:
* The validation split contains 60 tasks . The training split only contains 55 task because the 5 “Object Attributes” tasks are withheld from the training set.
* A subsampled version of the dataset (96 frames per episode) is available [here](https://huggingface.co/datasets/yali30/findingdory-subsampled-96).
📄 Citation
```
@article{yadav2025findingdory,
title = {FindingDory: A Benchmark to Evaluate Memory in Embodied Agents},
author = {Yadav, Karmesh and Ali, Yusuf and Gupta, Gunshi and Gal, Yarin and Kira, Zsolt},
journal = {arXiv preprint arXiv:2506.15635},
year = {2025}
}
``` |