Improve dataset card: Add paper/code links, description, sample usage, citation, and language tag

README.md

@@ -2,6 +2,8 @@
 license: apache-2.0
 task_categories:
 - robotics
+language:
+- en
 tags:
 - LeRobot
 configs:
@@ -13,10 +15,11 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
 
 ## Dataset Description
 
+This dataset provides the robotic trajectories and observations used in 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, enabling faster inference for robotic manipulation tasks. The datasets are built on [LeRobot](https://github.com/huggingface/lerobot) Hugging Face formats and optimized into offline `zarr` for faster training.
 
-
-- **Homepage:** [More Information Needed]
-- **Paper:** [More Information Needed]
+- **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
 
 ## Dataset Structure
@@ -159,11 +162,54 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
 }
 ```
 
+## Sample Usage
+
+This dataset is designed to be used with the VITA codebase, which extends [LeRobot](https://github.com/huggingface/lerobot). Below are examples for converting datasets to an optimized `zarr` format and training a VITA policy.
+
+First, ensure the VITA repository is cloned and setup, and the `FLARE_DATASETS_DIR` environment variable is set as described in the [VITA GitHub repository](https://github.com/ucd-dare/VITA#%EF%B8%8F-setup).
+
+### Dataset Preprocessing
+
+To list available datasets:
+```bash
+cd gym-av-aloha/scripts
+python convert.py --ls
+```
+
+To convert a HuggingFace dataset to an offline `zarr` format (e.g., `av_aloha_sim_hook_package`):
+```bash
+python convert.py -r iantc104/av_aloha_sim_hook_package
+```
+
+### Training a VITA Policy
+
+Once the dataset is converted, you can train a VITA policy using the `flare` module from the VITA codebase:
+```bash
+python flare/train.py policy=vita task=hook_package session=test
+```
+You can override default configurations as needed:
+```bash
+# Example: Use a specific GPU
+python flare/train.py policy=vita task=hook_package session=test device=cuda:2
+
+# Example: 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
+
+# Example: Run an ablation
+python flare/train.py policy=vita task=hook_package session=ablate \
+  policy.vita.decode_flow_latents=False wandb.notes=ablation
+```
 
 ## Citation
 
 **BibTeX:**
 
 ```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}
+}
 ```