PyPnC is a python library designed for generating trajectories for a robot system and stabilizing the system over the trajectories.

• Install anaconda
• Clone the repository:
  $ git clone https://github.com/junhyeokahn/PyPnC.git
• Create a virtual environment and install dependancies:
  $ conda env create -f pypnc.yml
• Activate the environment:
  $ conda activate pypnc

• Run the code:
  $ python simulator/pybullet/manipulator_main.py

• Run the code:
  $ python simulator/pybullet/atlas_dynamics_main.py
• Send walking commands through keystroke interface. For example, press 8 for forward walking, press 5 for in-place walking, press 4 for leftward walking, press 6 for rightward walking, press 2 for backward walking, press 7 for ccw turning, and press 9 for cw turning.
• Plot the results:
  $ python plot/atlas/plot_task.py --file=data/history.pkl

• For TOWR+, install additional dependancy ifopt
• Train a Composite Rigid Body Inertia network and generate files for optimization:
  $ python simulator/pybullet/atlas_crbi_trainer.py and press 5 for training
• Run TOWR+:
  $ mkdir build && cd build && cmake .. && make -j6 && ./atlas_forward_walk
• Plot the optimized trajectory:
  $ python plot/plot_towr_plus_trajectory.py --file=data/atlas_forward_walk.yaml --crbi_model_path=data/tf_model/atlas_crbi
• Replay the optimized trajectory with the robot:
  $ python simulator/pybullet/atlas_kinematics_main.py --file=data/atlas_forward_walk.yaml

@article{10.3389/frobt.2021.712239,
	author = {Ahn, Junhyeok and Jorgensen, Steven Jens and Bang, Seung Hyeon and Sentis, Luis},
	journal = {Frontiers in Robotics and AI},
	pages = {257},
	title = {Versatile Locomotion Planning and Control for Humanoid Robots},
	volume = {8},
	year = {2021}}