/tp_gmm

Task Parameterized Gaussian Mixture Model (TP-GMM) and Regression implemented purely on numpy

Primary LanguagePythonMIT LicenseMIT

Task-Parameterized-Gaussian-Mixture-Model

This is an clone of the original implementation of the Task-Parameterized-Gaussian-Mixture-Model by BatyaGG. The original implementation can be found here

In this implementation, I am creating a python package and organize a bit the code such that it can be easly used in other projects. The goal is to use this code as comparisons to other algorithms that I am developing. I do not have intention of contributing on TP-GMM.

Associated paper:

Alizadeh, T., & Saduanov, B. (2017, November). Robot programming by demonstration of multiple tasks within a common environment. In Multisensor Fusion and Integration for Intelligent Systems (MFI), 2017 IEEE International Conference on (pp. 608-613). IEEE.

All math, concepts and data are referred from the research publication and MATLAB implementation both by professor Sylvain Calinon (http://calinon.ch):

Calinon, S. (2016) A Tutorial on Task-Parameterized Movement Learning and Retrieval Intelligent Service Robotics (Springer), 9:1, 1-29.