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README.md

@@ -4,6 +4,11 @@ task_categories:
 - robotics
 tags:
 - LeRobot
+- gaze
+- foveated-vision
+- robot-learning
+- simulation
+library_name: lerobot
 configs:
 - config_name: default
   data_files: data/*/*.parquet
@@ -13,10 +18,11 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
 
 ## Dataset Description
 
+This dataset, presented in the paper [Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers](https://huggingface.co/papers/2507.15833), provides a simulation benchmark and dataset for training robot policies that incorporate human gaze. It includes bimanual robot demonstrations with synchronized human eye-tracking data collected using the AV-ALOHA simulation platform for the peg insertion task. This dataset is part of a larger effort to explore how human-like active gaze can enhance robot learning efficiency and robustness.
 
-
-- **Homepage:** [More Information Needed]
-- **Paper:** [More Information Needed]
+- **Homepage:** [https://ian-chuang.github.io/gaze-av-aloha/](https://ian-chuang.github.io/gaze-av-aloha/)
+- **Paper:** [https://huggingface.co/papers/2507.15833](https://huggingface.co/papers/2507.15833)
+- **Code:** [https://github.com/ian-chuang/gaze-av-aloha](https://github.com/ian-chuang/gaze-av-aloha)
 - **License:** apache-2.0
 
 ## Dataset Structure
@@ -108,76 +114,76 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
                 "has_audio": false
             }
         },
-        "observation.images.wrist_cam_right": {
-            "dtype": "video",
-            "shape": [
+        \"observation.images.wrist_cam_right\": {
+            \"dtype\": \"video\",
+            \"shape\": [
                 480,
                 640,
                 3
             ],
-            "names": [
-                "height",
-                "width",
-                "channel"
+            \"names\": [
+                \"height\",
+                \"width\",
+                \"channel\"
             ],
-            "info": {
-                "video.height": 480,
-                "video.width": 640,
-                "video.codec": "av1",
-                "video.pix_fmt": "yuv420p",
-                "video.is_depth_map": false,
-                "video.fps": 25,
-                "video.channels": 3,
-                "has_audio": false
+            \"info\": {
+                \"video.height\": 480,
+                \"video.width\": 640,
+                \"video.codec\": \"av1\",
+                \"video.pix_fmt\": \"yuv420p\",
+                \"video.is_depth_map\": false,
+                \"video.fps\": 25,
+                \"video.channels\": 3,
+                \"has_audio\": false
             }
         },
-        "observation.images.overhead_cam": {
-            "dtype": "video",
-            "shape": [
+        \"observation.images.overhead_cam\": {
+            \"dtype\": \"video\",
+            \"shape\": [
                 480,
                 640,
                 3
             ],
-            "names": [
-                "height",
-                "width",
-                "channel"
+            \"names\": [
+                \"height\",
+                \"width\",
+                \"channel\"
             ],
-            "info": {
-                "video.height": 480,
-                "video.width": 640,
-                "video.codec": "av1",
-                "video.pix_fmt": "yuv420p",
-                "video.is_depth_map": false,
-                "video.fps": 25,
-                "video.channels": 3,
-                "has_audio": false
+            \"info\": {
+                \"video.height\": 480,
+                \"video.width\": 640,
+                \"video.codec\": \"av1\",
+                \"video.pix_fmt\": \"yuv420p\",
+                \"video.is_depth_map\": false,
+                \"video.fps\": 25,
+                \"video.channels\": 3,
+                \"has_audio\": false
             }
         },
-        "observation.images.worms_eye_cam": {
-            "dtype": "video",
-            "shape": [
+        \"observation.images.worms_eye_cam\": {
+            \"dtype\": \"video\",
+            \"shape\": [
                 480,
                 640,
                 3
             ],
-            "names": [
-                "height",
-                "width",
-                "channel"
+            \"names\": [
+                \"height\",
+                \"width\",
+                \"channel\"
             ],
-            "info": {
-                "video.height": 480,
-                "video.width": 640,
-                "video.codec": "av1",
-                "video.pix_fmt": "yuv420p",
-                "video.is_depth_map": false,
-                "video.fps": 25,
-                "video.channels": 3,
-                "has_audio": false
+            \"info\": {
+                \"video.height\": 480,
+                \"video.width\": 640,
+                \"video.codec\": \"av1\",
+                \"video.pix_fmt\": \"yuv420p\",
+                \"video.is_depth_map\": false,
+                \"video.fps\": 25,
+                \"video.channels\": 3,
+                \"has_audio\": false
             }
         },
-        "observation.state": {
+        \"observation.state\": {
             "dtype": "float32",
             "shape": [
                 21
@@ -272,11 +278,50 @@ This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
 }
 ```
 
+## Sample Usage
+
+This dataset is provided in LeRobot format for ease of sharing and visualization. For faster access during training, it is recommended to convert the dataset to a custom `AVAlohaDataset` format based on Zarr.
+
+1.  **Install Dependencies:**
+    First, ensure you have the `lerobot` library and other necessary dependencies installed as described in the official GitHub repository.
+
+    ```bash
+    # Install LeRobot (if not already installed)
+    pip install git+https://github.com/huggingface/lerobot.git
+
+    # Clone the gaze-av-aloha repository for scripts and set up environment
+    git clone https://github.com/ian-chuang/gaze-av-aloha.git
+    cd gaze-av-aloha
+    # Follow additional installation steps from the repo's README, e.g., conda env setup
+    conda create -n gaze python=3.10
+    conda activate gaze
+    pip install -e ./gym_av_aloha
+    pip install -e ./gaze_av_aloha
+    ```
+
+2.  **Convert Dataset to Zarr Format:**
+    Use the conversion script provided in the GitHub repository to convert this dataset to the Zarr format:
+
+    ```bash
+    python gym_av_aloha/scripts/convert_lerobot_to_avaloha.py --repo_id iantc104/av_aloha_sim_peg_insertion
+    ```
+
+    Converted datasets will be saved under `gym_av_aloha/outputs/`.
+
+For more detailed usage, including training and evaluating policies, please refer to the [project's GitHub repository](https://github.com/ian-chuang/gaze-av-aloha).
 
 ## Citation
 
 **BibTeX:**
 
 ```bibtex
-[More Information Needed]
+@misc{chuang2025lookfocusactefficient,
+      title={Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers}, 
+      author={Ian Chuang and Andrew Lee and Dechen Gao and Jinyu Zou and Iman Soltani},
+      year={2025},
+      eprint={2507.15833},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2507.15833}, 
+}
 ```