ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback

This is part of official implementation of ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback (ICRA 2025)

For training diffusion policy using collected dataset please checkout link

For more details please check our website and paper

Data collection

Pre-requisite

1. Operating System: Windows 10/11 (Tested)
2. VR Headset: Meta Quest 3
3. RGB-D Camera: Intel realsense D435 camera [Optional to try our code, required to collect data]

Install python data collection server

cd data_processing

Install python packages

Python Version: 3.11

pip install -r requirements.txt

Setup local area network

1. Download and Install Rokoko Studio
2. Create a scene and connect Rokoko Smart Glove to Rokoko Studio
3. Enable data streaming, set forward IP to your computer's IP, set data format as JSONv3
4. Configure data_processing/ip_config.py set VR_HOST to VR headset IP address and LOCAL_HOST to your computer's IP

Setup hardware

1. 3D print camera mount.Camera Mount
2. 3D print glove mount. Glove Mount
3. Following instruction of this video to assemble mounts.
4. Connect D435 camera to computer using Type-C cable.

Install data collection APP on Quest 3

Install pre-built binary

1. Install Meta Quest Developer Hub link in your PC
2. Connect Quest 3 headset to your PC, open Meta Quest Developer Hub, device should be shown in MQDH. If not put on Quest 3, enable developer mode and enable usb debugging.
3. Install arcap_release.apk using MQDH -> Device Manager.

Build from source

1. Download Unity Hub / Editor link
2. Follow link to install Unity dependencies for quest development
3. In Unity Hub, Add->Add project from disk, select: ARCap_Unity
4. Open ARCap_unity, waiting for Unity Editor to open
5. Deploy APP on Quest 3:
   • Top menu bar->File->Build Settings, select Android, select Quest 3 as device, Click Build And Run

Start data collection

Collect data

In data_processing folder, mkdir data

1. Leap hand: in data_processing folder, run:

python data_collection_server.py # --no_camera if D435 is not connected

2. Parallel jaw gripper: in data_processing folder, run:

python data_collection_server.py --handedness left # --no_camera if D435 is not connected

All collected data will be stored in data/<yyyy-mm-dd-hh-mm-ss>, each trajectory has its own folder named: data/<yyyy-mm-dd-hh-mm-ss>/<yyyy-mm-dd-hh-mm-ss>. Each frame is stored as a .npz

Post-process data to robomimic compatible format

In data_processing folder, mkdir data_processed

1. Leap hand: in data_processing folder, run:

python convert_data_with_robot.py --root_path <yyyy-mm-dd-hh-mm-ss> --visualize

2. Parallel jaw gripper: in data_processing folder, run:

python convert_data_with_robot.py --root_path <yyyy-mm-dd-hh-mm-ss> --use_gripper --visualize

All processed data will be stored in data_processed/<yyyy-mm-dd-hh-mm-ss>, each trajectory has its own folder, named: data_processed/<yyyy-mm-dd-hh-mm-ss>/demo_i. Each frame is stored as a subfolder data_processed/<yyyy-mm-dd-hh-mm-ss>/demo_i/frame_j, which contains joint angles of hand and arm, wrist poses and point clouds

Citation

If you find this repository useful please cite:

@article{chen2024arcap,
    title={ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback},
    author={Chen, Sirui and Wang, Chen and Nguyen, Kaden and Fei-Fei, Li and Liu, C Karen},
    journal={arXiv preprint arXiv:2410.08464},
    year={2024}
}