This codebase has been released as a part of OpenTeach
Clone the repository using the following command.
git clone https://github.com/NYU-robot-learning/Allegro-Sim-Policies.git
Installation
mamba env create -f conda_env.yml
Install the Codebase as a module using
pip install -e .
Collect Demos using OpenTeach.
- Change the path in configs to the path where you saved your data.
- Preprocess the data using the following command
python3 preprocess.py
- Choose the path you have collected data in configs in data_dir:. Choose the data representations you want to use for training from
image , allegro
- Once preprocessed you can train the Vision and tactile encoder using python train.py. You can edit train.yaml accordingly with the choice of encoder, rl_learners, rewarders and optimizers.
- After training the Vision and tactile encoders you can start the offset learning following TAVI using
python train_online_sim.py
. You can set the task, base_policy,agent, explorer and rewarder. configs
If you use this repo in your research, please consider citing the paper as follows:
title={OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation},
author={Aadhithya Iyer and Zhuoran Peng and Yinlong Dai and Irmak Guzey and Siddhant Haldar and Soumith Chintala and Lerrel Pinto},
year={2024},
eprint={2403.07870},
archivePrefix={arXiv},
primaryClass={cs.RO}
}