C++ library for Function Approximation, Dynamical Movement Primitives, and Black-Box Optimization
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This repository provides an implementation of dynamical systems, function approximators, dynamical movement primitives, and black-box optimization with evolution strategies, in particular the optimization of the parameters of dynamical movement primitives.

A PDF tutorial on these topics (generated with Doxygen) is available at https://github.com/stulp/dmpbbo/blob/master/docs/tutorial.pdf

A snapshot of the complete HTML doxygen documentation is here: http://freekstulp.net/dmpbbo/html/

For whom?

This library may be useful for you if you

  • are new to dynamical movement primitives and want to learn about them (see the tutorial in the doxygen documentation).

  • already know about dynamical movement primitives, but would rather use existing, tested code than brew it yourself.

  • want to do reinforcement learning/optimization of dynamical movement primitives.

Most submodules of this project are independent of all others, so if you don't care about dynamical movement primitives, the following submodules can still easily be integrated in other code to perform some (hopefully) useful function:

  • functionapproximators/ : a module that defines a generic interface for function approximators, as well as several specific implementations (LWR, LWPR, iRFRLS, GMR)

  • dynamicalsystems/ : a module that defines a generic interface for dynamical systems, as well as several specific implementations (exponential, sigmoid, spring-damper)

  • bbo/ : implementation of some (rather simple) algorithms for the stochastic optimization of black-box cost functions

If you use this library in the context of experiments for a scientific paper, we would appreciate if you could cite this library in the paper as follows:

    author = {Freek Stulp},
    title  = {{\tt DmpBbo} -- A C++ library for black-box optimization of 
                                                dynamical movement primitives.},
    year   = {2014},
    url    = {https://github.com/stulp/dmpbbo.git}


How to install the libraries/binaries/documentation is described in INSTALL.txt

To learn how to use the code, the first thing to do is look at the documentation and tutorial here:

  • build_dir/docs/html/index.html This documentation must first be generated with doxygen, see INSTALL.txt

  • docs/tutorial.pdf This is a snapshot of the PDF in docs/tutorial/

To delve into the code a bit deeper, each module has a set of demos, e.g.

  • demos/dynamicalsystems/ The demos do not show all the functionality, but are well documented and a good place to understand how the code can be used. There are python scripts that call the right executables, and do some plotting.


For our own use, the aims of coding this were the following:

  • Allowing easy and modular exchange of different dynamical systems within dynamical movement primitives.

  • Allowing easy and modular exchange of different function approximators within dynamical movement primitives.

  • Being able to compare different exploration strategies (e.g. covariance matrix adaptation vs. exploration decay) when optimizing dynamical movement primitives.

  • Enabling the optimization of different parameter subsets of function approximators.

  • Running dynamical movement primitives on real robots.

Why Python and C++?

A part of the functionality of the C++ code has been mirrored in Python. The Python version is probably the better language for getting to know dmpbbo (especially if you do not know C++ ;-) The C++ code is the better choice if you want to run dmpbbo on a real robot in a real-time environment.

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