Datasets:
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- gaze
- foveated-vision
- robot-learning
- simulation
library_name: lerobot
configs:
- config_name: default
data_files: data/*/*.parquet
This dataset was created using LeRobot.
Dataset Description
This dataset, presented in the paper Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers, 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: https://ian-chuang.github.io/gaze-av-aloha/
- Paper: https://huggingface.co/papers/2507.15833
- Code: https://github.com/ian-chuang/gaze-av-aloha
- License: apache-2.0
Dataset Structure
{
"codebase_version": "v2.1",
"robot_type": null,
"total_episodes": 100,
"total_frames": 17741,
"total_tasks": 1,
"total_videos": 600,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 25,
"splits": {
"train": "0:100"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"observation.images.zed_cam_left": {
"dtype": "video",
"shape": [
480,
640,
3
],
"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
}
},
"observation.images.zed_cam_right": {
"dtype": "video",
"shape": [
480,
640,
3
],
"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
}
},
"observation.images.wrist_cam_left": {
"dtype": "video",
"shape": [
480,
640,
3
],
"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
}
},
\"observation.images.wrist_cam_right\": {
\"dtype\": \"video\",
\"shape\": [
480,
640,
3
],
\"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
}
},
\"observation.images.overhead_cam\": {
\"dtype\": \"video\",
\"shape\": [
480,
640,
3
],
\"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
}
},
\"observation.images.worms_eye_cam\": {
\"dtype\": \"video\",
\"shape\": [
480,
640,
3
],
\"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
}
},
\"observation.state\": {
"dtype": "float32",
"shape": [
21
],
"names": null
},
"observation.environment_state": {
"dtype": "float32",
"shape": [
14
],
"names": null
},
"action": {
"dtype": "float32",
"shape": [
21
],
"names": null
},
"left_eye": {
"dtype": "float32",
"shape": [
2
],
"names": null
},
"right_eye": {
"dtype": "float32",
"shape": [
2
],
"names": null
},
"left_arm_pose": {
"dtype": "float32",
"shape": [
16
],
"names": null
},
"right_arm_pose": {
"dtype": "float32",
"shape": [
16
],
"names": null
},
"middle_arm_pose": {
"dtype": "float32",
"shape": [
16
],
"names": null
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}
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.
Install Dependencies: First, ensure you have the
lerobotlibrary and other necessary dependencies installed as described in the official GitHub repository.# 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_alohaConvert Dataset to Zarr Format: Use the conversion script provided in the GitHub repository to convert this dataset to the Zarr format:
python gym_av_aloha/scripts/convert_lerobot_to_avaloha.py --repo_id iantc104/av_aloha_sim_peg_insertionConverted 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.
Citation
BibTeX:
@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},
}