Update README.md (#2)

- Update README.md (d9cd3f126d3592891c81121d4630dfec0d9debcb)


Co-authored-by: Karmesh Yadav <[email protected]>

README.md

@@ -33,4 +33,74 @@ configs:
     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 Subsampled Video  Dataset. Each video contains 96 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-subsampled-96")
+```
+
+# 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.
+* The full video version of the dataset is available [here](https://huggingface.co/datasets/yali30/findingdory).
+
+📄 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}
+}
+```