Skip to content

Latest commit

 

History

History
162 lines (125 loc) · 5.58 KB

README.md

File metadata and controls

162 lines (125 loc) · 5.58 KB
Raw

Open-TeleVision: Teleoperation with

Immersive Active Visual Feedback

Xuxin Cheng* · Jialong Li* · Shiqi Yang
Ge Yang · Xiaolong Wang

                   

Website | arXiv | Video | Summary

Introduction

This code contains implementation for teleoperation and imitation learning of Open-TeleVision.

Installation

    conda create -n tv python=3.8
    conda activate tv
    pip install -r requirements.txt
    cd act/detr && pip install -e .

Install ZED sdk: https://www.stereolabs.com/developers/release/

Install ZED Python API:

    cd /usr/local/zed/ && python get_python_api.py

If you want to try teleoperation example in a simulated environment (teleop_hand.py):

Install Isaac Gym: https://developer.nvidia.com/isaac-gym/

Teleoperation Guide

Local streaming

For Quest local streaming, follow this issue.

Apple does not allow WebXR on non-https connections. To test the application locally, we need to create a self-signed certificate and install it on the client. You need a ubuntu machine and a router. Connect the VisionPro and the ubuntu machine to the same router.

  1. install mkcert: https://github.com/FiloSottile/mkcert
  2. check local ip address:
    ifconfig | grep inet

Suppose the local ip address of the ubuntu machine is 192.168.8.102.

  1. create certificate:
    mkcert -install && mkcert -cert-file cert.pem -key-file key.pem 192.168.8.102 localhost 127.0.0.1

ps. place the generated cert.pem and key.pem files in teleop.

  1. open firewall on server
    sudo iptables -A INPUT -p tcp --dport 8012 -j ACCEPT
    sudo iptables-save
    sudo iptables -L

or can be done with ufw:

    sudo ufw allow 8012

    tv = OpenTeleVision(self.resolution_cropped, shm.name, image_queue, toggle_streaming, ngrok=False)
  1. install ca-certificates on VisionPro
    mkcert -CAROOT

Copy the rootCA.pem via AirDrop to VisionPro and install it.

Settings > General > About > Certificate Trust Settings. Under "Enable full trust for root certificates", turn on trust for the certificate.

settings > Apps > Safari > Advanced > Feature Flags > Enable WebXR Related Features

  1. open the browser on Safari on VisionPro and go to https://192.168.8.102:8012?ws=wss://192.168.8.102:8012

  2. Click Enter VR and Allow to start the VR session.

Network Streaming

For Meta Quest3, installation of the certificate is not trivial. We need to use a network streaming solution. We use ngrok to create a secure tunnel to the server. This method will work for both VisionPro and Meta Quest3.

  1. Install ngrok: https://ngrok.com/download
  2. Run ngrok
    ngrok http 8012
  1. Copy the https address and open the browser on Meta Quest3 and go to the address.

ps. When using ngrok for network streaming, remember to call OpenTeleVision with:

    self.tv = OpenTeleVision(self.resolution_cropped, self.shm.name, image_queue, toggle_streaming, ngrok=True)

Simulation Teleoperation Example

  1. After setup up streaming with either local or network streaming following the above instructions, you can try teleoperating two robot hands in Issac Gym:
    cd teleop && python teleop_hand.py

  1. Go to your vuer site on VisionPro, click Enter VR and Allow to enter immersive environment.

  2. See your hands in 3D!

Training Guide

  1. Download dataset from https://drive.google.com/drive/folders/11WO96mUMjmxRo9Hpvm4ADz7THuuGNEMY?usp=sharing.

  2. Place the downloaded dataset in data/recordings/.

  3. Process the specified dataset for training using scripts/post_process.py.

  4. You can verify the image and action sequences of a specific episode in the dataset using scripts/replay_demo.py.

  5. To train ACT, run:

    python imitate_episodes.py --policy_class ACT --kl_weight 10 --chunk_size 60 --hidden_dim 512 --batch_size 45 --dim_feedforward 3200 --num_epochs 50000 --lr 5e-5 --seed 0 --taskid 00 --exptid 01-sample-expt
  1. After training, save jit for the desired checkpoint:
    python imitate_episodes.py --policy_class ACT --kl_weight 10 --chunk_size 60 --hidden_dim 512 --batch_size 45 --dim_feedforward 3200 --num_epochs 50000 --lr 5e-5 --seed 0 --taskid 00 --exptid 01-sample-expt\
                               --save_jit --resume_ckpt 25000
  1. You can visualize the trained policy with inputs from dataset using scripts/deploy_sim.py, example usage:
    python deploy_sim.py --taskid 00 --exptid 01 --resume_ckpt 25000

Citation

@article{cheng2024tv,
title={Open-TeleVision: Teleoperation with Immersive Active Visual Feedback},
author={Cheng, Xuxin and Li, Jialong and Yang, Shiqi and Yang, Ge and Wang, Xiaolong},
journal={arXiv preprint arXiv:2407.01512},
year={2024}
}