Update dataset card: add paper, project page, code, sample usage, flow-matching tag, and citation

README.md

@@ -4,19 +4,21 @@ task_categories:
 - robotics
 tags:
 - LeRobot
+- flow-matching
 configs:
 - config_name: default
   data_files: data/*/*.parquet
 ---
 
-This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
+This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). It contains data associated with the paper [VITA: Vision-to-Action Flow Matching Policy](https://huggingface.co/papers/2507.13231).
 
 ## Dataset Description
 
+This dataset is associated with the paper [VITA: Vision-to-Action Flow Matching Policy](https://huggingface.co/papers/2507.13231). VITA introduces a noise-free and conditioning-free policy learning framework that directly maps visual representations to latent actions using flow matching. This dataset comprises the data used for evaluating VITA on 8 simulation and 2 real-world tasks from ALOHA and Robomimic.
 
-
-- **Homepage:** [More Information Needed]
-- **Paper:** [More Information Needed]
+- **Homepage:** [https://ucd-dare.github.io/VITA/](https://ucd-dare.github.io/VITA/)
+- **Paper:** [https://huggingface.co/papers/2507.13231](https://huggingface.co/papers/2507.13231)
+- **Code:** [https://github.com/ucd-dare/VITA](https://github.com/ucd-dare/VITA)
 - **License:** apache-2.0
 
 ## Dataset Structure
@@ -119,7 +121,7 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
                 "video.channels": 3,
                 "has_audio": false
             }
-        },
+        },\
         "timestamp": {
             "dtype": "float32",
             "shape": [
@@ -159,11 +161,58 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
 }
 ```
 
+## Sample Usage
 
-## Citation
+The datasets are designed to be used with the VITA codebase, which extends [LeRobot](https://github.com/huggingface/lerobot) for optimized preprocessing and training.
+
+First, set up the VITA environment as described in the [Github repository](https://github.com/ucd-dare/VITA):
+```bash
+git clone git@github.com:ucd-dare/VITA.git
+cd VITA
+conda create --name vita python==3.10
+conda activate vita
+conda install cmake
+pip install -e .
+pip install -r requirements.txt
+# Install LeRobot dependencies
+cd lerobot
+pip install -e .
+# Install ffmpeg for dataset processing
+conda install -c conda-forge ffmpeg
+```
+
+Set the dataset storage path:
+```bash
+echo 'export FLARE_DATASETS_DIR=<PATH_TO_VITA>/gym-av-aloha/outputs' >> ~/.bashrc
+# Reload bashrc
+source ~/.bashrc
+conda activate vita
+```
 
-**BibTeX:**
+You can list available datasets (hosted on Hugging Face) using the conversion script:
+```bash
+cd gym-av-aloha/scripts
+python convert.py --ls
+```
+
+To convert a Hugging Face dataset to the optimized offline Zarr format for faster training (this may take >10 minutes), for example:
+```bash
+python convert.py -r iantc104/av_aloha_sim_hook_package
+```
+Converted datasets will be stored in the path specified by `FLARE_DATASETS_DIR`.
+
+To train a policy using a task (e.g., `hook_package`) with the VITA framework:
+```bash
+python flare/train.py policy=vita task=hook_package session=test
+```
+
+## Citation
 
 ```bibtex
-[More Information Needed]
+@article{gao2025vita,
+  title={VITA: Vision-to-Action Flow Matching Policy},
+  author={Gao, Dechen and Zhao, Boqi and Lee, Andrew and Chuang, Ian and Zhou, Hanchu and Wang, Hang and Zhao, Zhe and Zhang, Junshan and Soltani, Iman},
+  journal={arXiv preprint arXiv:2507.13231},
+  year={2025}
+}
 ```