C++/python codes for contact-implicit trajectory optimization for locomotion and manipulation.

• Ubuntu 20.04+
• CMake 3.15+
• Python 3.6+
• C++11
• pybind11
• Eigen3
• casadi (SHA "402fe583f0d3cf1fc77d1e1ac933f75d86083124"). Note that when you build casadi from source, make sure you do cmake -DWITH_IPOPT=true.
• param, originally from here
• matplotlib-cpp. Note that please install this package in your (native) environment.

• The easiest way to install is through CMake. We first install the dependencies:

sudo apt-get install python3-dev cmake libeigen3-dev

• Then, you need to install casadi and param. For param installation, see this.
• For casadi, you can also follow this. Note that if you are interested in using optimization solvers such as gurobi, which are not compiled as a default CMake setting of casadi, you need to recompile casadi. Or you can simply run the following commands:

git clone https://github.com/casadi/casadi.git
cd casadi; mkdir build; cd build
cmake ..
cmake --build .
sudo make install

• Finally, we are ready to build and install ContactRichOpt. To do that, like how we installed casadi, we use CMake as follows:

git clone https://github.com/YukiShirai/ContactRichOpt.git
cd casadi; mkdir build; cd build
cmake ..
cmake --build .
sudo make install

• and that's it! Note that if your CMake project complains during your cmake .. like:

/usr/bin/ld: cannot find -lContactRichOpt

• then check where your libContactRichOpt.so was installed. It can be /usr/local/lib. If that's the case, crate symbolic link as follows:

sudo ln -s /usr/local/lib/libContactRichOpt.so /usr/lib/libContactRichOpt.so

conpt supports python binding, pyconpt, through pybind11.

1. Set up virtual environment in conpt/build:

sudo pip3 install virtualenv
virtualenv -p /usr/bin/python3 env
source env/bin/activate
pip install numpy

2. After you finish installing conpt from source, pyconpt should be ready to be used. For example,

from pyconpt import opt

cartpole = opt.CartPole()

The easiest way is using CMake.

1. To call ContactRichOpt in your CMake project, you can do as follows:

find_package(ContactRichOpt REQUIRED)

target_link_libraries(BINARY_NAME
	                    PUBLIC
		                  ContactRichOpt)

2. Then, in your c++ code,

#include "conpt/CartPole.h"

• [] support other toy problems
• [] support python bindings using pybind11.
• [] support mixed-integer programming through gurobi
• [] support stochastic optimization such as chance-constrained optimization.
• [] integrate with simulator such as pybullet / drake.
• [] support MPC
• [] support bilevel optimization
• [] improve customizability. user should be able to add any constraints through main.cpp.

@misc{shirai2023ContactRichOpt,
  author = {Yuki Shirai},
  title = {libContactRichOpt},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/YukiShirai/ContactRichOpt}}
}

If you have any questions, feel free to ask questions via issues or email.

@YukiShirai ([email protected])

@ColinTogashi for discussion about CMake and pybind11.

[1] Y. Shirai, D. K. Jha, A. U. Raghunathan, D. Romeres, "Chance-Constrained Optimization in Contact-Rich Systems for Robust Manipulation", ACC2023

[2] A. U. Raghunathan, D. K. Jha and D. Romeres, "PyROBOCOP: Python-based Robotic Control & Optimization Package for Manipulation", ICRA2022

[3] Y. Shirai, D. K. Jha, A. U. Raghunathan and D. Romeres, "Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization", ICRA2022

[4] Y. Shirai et al., "Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots", IROS2022

[5] Y. Shirai, X. Lin, Y. Tanaka, A. Mehta and D. Hong, "Risk-Aware Motion Planning for a Limbed Robot with Stochastic Gripping Forces Using Nonlinear Programming", IEEE Robotics and Automation Letters, 2020