|
|
--- |
|
|
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} |
|
|
} |
|
|
``` |
