This is the official implementation of paper:

Hand-Object Interaction Controller (HOIC): Deep Reinforcement Learning for Reconstructing Interactions with Physics

1. Clone repository and create Conda (https://www.anaconda.com/) environment

   git clone https://github.com/hu-hy17/HOIC.git
   conda create -n HOIC python=3.8
   conda activate HOIC

2. Install physics simulator Mujoco210 (https://mujoco.org/)

   • For Windows:

     • Download from https://github.com/google-deepmind/mujoco/releases/download/2.1.0/mujoco210-windows-x86_64.zip
     • Extract mujoco210 folder to the root folder of this project.

   • For Linux:

     • Download from https://github.com/google-deepmind/mujoco/releases/download/2.1.0/mujoco210-linux-x86_64.tar.gz

     • Create .mujoco folder under /home and extract mujoco210 folder into .mujoco

     • Edit~/.bashrc and add the following environmental variables:

       export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/{username}/.mujoco/mujoco210/bin
       export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia

     • You may need to install GL libraries if you meet this error: fatal error: GL/gl.h: No such file or directory

       sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3

3. Install Pytorch (https://pytorch.org/)

   conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge

4. Install other dependencies

   pip install -r requirements.txt

1. Download training and testing dataset from https://drive.google.com/file/d/1XyljeKuDSE0WSV5NPXF02GRkDFUjeMeQ/view?usp=sharing . Extract SingleDepth folder under sample_data folder.

2. Download pre-trained weights from https://drive.google.com/file/d/1mNWecKFdkqeuz_3bUqWbWSlFVfH6tcDI/view?usp=sharing. Extract motion_im folder under results folder.

3. After the first two steps, your project structure looks like this:

   HOIC
   ├── assets
   ├── config
   ├── InferenceServer
   ├── mujoco210
   │   ├── bin
   │   ├── include
   │   ├── model
   │   └── sample
   ├── results
   │   └── motion_im
   │       ├── banana_future5_light_add_geom
   │       ├── bottle_future5_light_add_geom
   │       └── box_future5_light_add_geom
   ├── sample_data
   │   └── SingleDepth
   │       ├── Banana
   │       ├── Bottle
   │       └── Box
   ├── scripts
   └── uhc
   

4. Run the following command under the project root folder:

   python eval_handmimic.py --cfg box_future5_light_add_geom --epoch 4000

   where box_future5_light_add_geom is the config for Box object, it can be replaced with bottle_future5_light_add_geom and banana_future5_light_add_geom

1. Download training and testing dataset from . Extract SingleDepth folder under sample_data folder. (The same as testing)

2. Run the following command under the project root folder:

   python train_hand_mimic.py --cfg box_future5_light_add_geom --num_threads 32 --gpu_index 0

   where the option --num_threads and option --gpu_index can be set depending on your hardware, but currently this project only support one GPU for training.

   By default, the checkpoint will be save under

   Note: Currently this project only support single thread training on Windows platform, which is very slow (There is something wrong with multiprocessing library on Windows). For higher speed, please use Linux platform for training.

@article{hu2024hand,
  title={Hand-Object Interaction Controller (HOIC): Deep Reinforcement Learning for Reconstructing Interactions with Physics},
  author={Hu, Haoyu and Yi, Xinyu and Cao, Zhe and Yong, Jun-Hai and Xu, Feng},
  journal={arXiv preprint arXiv:2405.02676},
  year={2024}
}

• This repository is built upon UHC: Universal Humanoid Controller