Update dataset card for VITA: correct links, add code, sample usage, abstract, and citation

README.md

@@ -1,9 +1,10 @@
 ---
-license: mit
+license: apache-2.0
 task_categories:
 - robotics
 tags:
 - LeRobot
+- flow-matching
 configs:
 - config_name: default
   data_files: data/*/*.parquet
@@ -13,11 +14,70 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
 
 ## Dataset Description
 
+This dataset contains demonstration data for robotic tasks, utilized by **VITA: Vision-to-Action Flow Matching Policy**. VITA is a noise-free and conditioning-free policy learning framework that directly maps visual representations to latent actions using flow matching. It treats latent visual representations as the source of the flow, eliminating the need for conditioning. VITA is evaluated on 8 simulation and 2 real-world tasks from ALOHA and Robomimic, outperforming or matching state-of-the-art generative policies while achieving faster inference.
 
+- **Homepage:** https://ucd-dare.github.io/VITA/
+- **Paper:** https://huggingface.co/papers/2507.13231
+- **Code:** https://github.com/ucd-dare/VITA
+- **License:** Apache 2.0
 
-- **Homepage:** https://diffusion-policy.cs.columbia.edu/
-- **Paper:** https://arxiv.org/abs/2303.04137v5
-- **License:** mit
+## Sample Usage
+
+To get started with VITA and use this dataset, follow these steps to set up the environment, preprocess datasets, and train a policy, as described in the [VITA GitHub repository](https://github.com/ucd-dare/VITA).
+
+First, clone the repository and set up the conda environment:
+```bash
+git clone [email protected]: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 (replace `<PATH_TO_VITA>` with the absolute path to your cloned VITA directory):
+```bash
+echo 'export FLARE_DATASETS_DIR=<PATH_TO_VITA>/gym-av-aloha/outputs' >> ~/.bashrc
+source ~/.bashrc
+conda activate vita
+```
+
+Install benchmark dependencies for AV-ALOHA and/or Robomimic as needed:
+```bash
+# For AV-ALOHA
+cd gym-av-aloha
+pip install -e .
+# For Robomimic
+cd gym-robomimic
+pip install -e .
+```
+
+To convert a HuggingFace dataset to offline zarr format (e.g., `iantc104/av_aloha_sim_hook_package`):
+```bash
+cd gym-av-aloha/scripts
+python convert.py -r iantc104/av_aloha_sim_hook_package
+```
+The converted datasets will be stored in `./gym-av-aloha/outputs`.
+
+To train a VITA policy, use the `flare/train.py` script:
+```bash
+python flare/train.py policy=vita task=hook_package session=test
+```
+You can customize training with various flags, for example:
+```bash
+# Use a specific GPU
+python flare/train.py policy=vita task=hook_package session=test device=cuda:2
+
+# Change online validation frequency and episodes
+python flare/train.py policy=vita task=hook_package session=test \
+  val.val_online_freq=2000 val.eval_n_episodes=10
+```
 
 ## Dataset Structure
 
@@ -143,16 +203,15 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
 }
 ```
 
-
 ## Citation
 
 **BibTeX:**
 
 ```bibtex
-@article{chi2024diffusionpolicy,
-    author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
-    title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
-    journal = {The International Journal of Robotics Research},
-    year = {2024},
+@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}
 }
 ```