PbDlib
PbDlib is a collection of source codes for robot programming by demonstration (learning from
demonstration). It includes various functionalities at the crossroad of statistical learning, dynamical
systems, differential geometry and optimal control. It is available in the following languages:
PbDlib can be used in applications requiring task adaptation, human-robot skill transfer, safe controllers based on minimal intervention principle, as well as for probabilistic motion analysis and synthesis in multiple coordinate systems.
Three distinct versions are maintained that can be used independently in Matlab, C++ or Python with independent git repositories. Currently, the Matlab version has the most functionalities. The C++ and Python versions are better suited for integration in robot applications. Each git page provides detailed instructions and list of examples.
PbDlib is currently maintained by Sylvain Calinon (sylvain.calinon@idiap.ch).
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Matlab version |
Git repository:
https://gitlab.idiap.ch/rli/pbdlib-matlab/Most examples of the Matlab version are compatible with the GNU Octave open source software.
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C++ version |
Git repository:
https://gitlab.idiap.ch/rli/pbdlib-cpp/The C++ version can be built with minimal dependency to external libraries, in order to facilitate its inclusion in other softwares.
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Python version by Dr Emmanuel Pignat (task-parameterized models, learning from demonstration) |
Git repository:
https://gitlab.idiap.ch/rli/pbdlib-python| |
Python version by Dr Martijn Zeestraten (Riemannian manifolds) |