Intro

Official implementation of "Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation" Robotics:Science and Systems (RSS 2022)

[Project page] [Paper]

Dependencies

Set conda environment

conda create -n quadruped_nav python=3.8
conda activate quadruped_nav

Install torch(1.10.1), numpy(1.21.2), matplotlib, scipy, ruamel.yaml

conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
conda install numpy=1.21.2
conda install matplotlib
conda install scipy
pip install ruamel.yaml

Install wandb and login. 'wandb' is a logging system similar to 'tensorboard'.

pip install wandb
wandb login

Install required python packages to compute Dynamic Time Warping in Parallel

pip install dtw-python
pip install fastdtw
pip install joblib

Install OMPL (Open Motion Planning Library). Python binding version of OMPL is used.

Download OMPL installation script in https://ompl.kavrakilab.org/installation.html.
chmod u+x install-ompl-ubuntu.sh
./install-ompl-ubuntu.sh --python

Simulator setup

RaiSim is used. Install it following the installation guide.

Then, set up RaisimGymTorch as following.

cd /RAISIM_DIRECTORY_PATH/raisimLib
git clone git@github.com:awesomericky/complex-env-navigation.git
cd complex-env-navigation
python setup.py develop

Path setup

Configure following paths. Parts that should be configured is set with TODO: PATH_SETUP_REQUIRED flag.

Project directory
- cfg['path']['home'] in /RAISIM_DIRECTORY_PATH/raisimLib/complex-env-navigation/raisimGymTorch/env/envs/test/cfg.yaml
OMPL Python binding
- OMPL_PYBIND_PATH in /RAISIM_DIRECTORY_PATH/raisimLib/complex-env-navigation/raisimGymTorch/env/envs/train/global_planner.py

Train model

Set logging: True in /RAISIM_DIRECTORY_PATH/raisimLib/complex-env-navigation/raisimGymTorch/env/envs/train/cfg.yaml, if you want to enable wandb logging.

Train Forward Dynamics Model (FDM).

Click 'c' to continue when pdb stops the code
To quit the training, click 'Ctrl + c' to call pdb. Then click 'q'.
Path of the trained velocity command tracking controller should be given with -tw flag.
Evaluations of FDM are visualized in /RAISIM_DIRECTORY_PATH/raisimLib/complex-env-navigation/trajectory_prediction_plot.

python raisimGymTorch/env/envs/train/FDM_train.py -tw /RAISIM_DIRECTORY_PATH/raisimLib/complex-env-navigation/data/command_tracking_flat/final/full_16200.pt

Download data to train Informed Trajectory Sampler (386MB) [link]

# Unzip the downloaded zip file and move it to required path.
unzip analytic_planner_data.zip
mv analytic_planner_data /RAISIM_DIRECTORY_PATH/raisimLib/complex-env-navigation/.

Train Informed Trajectory Sampler (ITS)

Click 'c' to continue when pdb stops the code.
To quit the training, click 'Ctrl + c' to call pdb. Then click 'q'.
Path of the trained Forward Dynamics Model should be given with -fw flag.

python raisimGymTorch/env/envs/train/ITS_train.py -fw /RAISIM_DIRECTORY_PATH/raisimLib/complex-env-navigation/data/FDM_train/XXX/full_XXX.pt

Run demo

Configure the trained weight paths (cfg['path']['FDM'] and cfg['path']['ITS']) in /RAISIM_DIRECTORY_PATH/raisimLib/complex-env-navigation/raisimGymTorch/env/envs/test/cfg.yaml. Parts that should be configured is set with TODO: WEIGHT_PATH_SETUP_REQUIRED flag.

Open RaiSim Unity to see the visualized simulation.

Run point-goal navigation with trained weight (click 'c' to continue when pdb stops the code)

python raisimGymTorch/env/envs/test/pgn_runner.py

Run safety-remote control with trained weight (click 'c' to continue when pdb stops the code)

python raisimGymTorch/env/envs/test/src_runner.py

To quit running the demo, click 'Ctrl + c' to call pdb. Then click 'q'.

Extra notes

This repository is not maintained anymore. If you have a question, send an email to awesomericky@kaist.ac.kr.
We don't take questions regarding installation. If you install the dependencies successfully, you can easily run this.
For the codes in rsc/, ANYbotics' license is applied. MIT license otherwise.
More details of the provided velocity command tracking controller for quadruped robots in flat terrain can be found in this paper and repository.

Cite

@INPROCEEDINGS{Kim-RSS-22, 
    AUTHOR    = {Yunho Kim AND Chanyoung Kim AND Jemin Hwangbo}, 
    TITLE     = {{Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2022}, 
    ADDRESS   = {New York City, NY, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2022.XVIII.069} 
}

awesomericky / complex-env-navigation