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ProMP C++

License: LGPL v3

This is a library in C++ that implements Probabilistic Motion Primitives algorithms. References :

  • Paraschos A, Daniel C, Peters J, Neumann G. Probabilistic movement primitives. Advances in neural information processing systems. 2013. [pdf]
  • Paraschos A, Daniel C, Peters J, Neumann G. Using probabilistic movement primitives in robotics. Autonomous Robots. 2018 Mar;42(3):529-51. [pdf].

Initially, it was inspired in the following python implementations:

Requirements

  • C++ 11
  • CMake 3.11
  • Eigen 3.2.92
  • Doxygen 1.8.11 (optional)
  • Python (optional)

(Other versions of CMake, Eigen, Doxygen will probably work, but those are the tested ones so far)

Installation

git clone --recursive https://gitlab.inria.fr/H2020-AnDy/promp.git
cd promp
mkdir build
cd build
cmake ..

If you wish to compile the python library, set BUILD_PYTHON_MODULE to ON before compiling the library.

If you wish to build API documentation, set BUILD_DOCUMENTATIONS to ON before compiling the library (this requires doxygen and doxybook2).

Then, proceed to make and install:

make
sudo make install

If while executing your compiled targets you get an error like the following:

cannot open shared object file: No such file or directory

Please try running ldconfig before running again:

sudo ldconfig

At the moment, the library has been tested on Ubuntu 16.04 and 18.04.

Usage

You can refer to the source code of some examples in the examples folder to understand the usage of the library. Their binaries are automatically generated after the installation.

The api is described in doc/api.md

In the folder etc/demos/ you can find many recordings of human whole-body postures (i.csv i=1,2,...) and/or body segments poses (pi.csv i=1,2,...) for several motions. With these you can train and play with your own ProMPs. Have fun!

Python module

Python module is built using pybind11. Be sure to get it with git submodule update --init --recursive before using cmake.

Eigen matrices are converted to numpy matrices. Because of the different interal data storage order, the conversion between them implies a performance overhead.

After sudo make install, the module is automatically installed to Python site-lib folder.

In your python code, you can just import the module and use it as in C++. You can find a .py script in the example folder.

import promp
...
file_list = ["t1.csv", "t2.csv"]
dofs = [1,2,3]

trajectory_group = promp.TrajectoryGroup()
trajectory_group.load_csv_trajectories(file_list, dofs)
trajectory_group.normalize_length()

my_promp = promp.Promp(trajectory_group.trajectories(), 20, 0.05)
gen_traj = my_promp.generate_trajectory()
gen_stddev = my_promp.gen_traj_std_dev()
gen_covariance = my_promp.generate_trajectory_covariance()

Acknowledgments

The development of this software is partially supported by the European Project H2020 An.Dy.