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Try it online: Binder

GMM-lbd

Gaussian mixture experiments for learning by demonstration

Notebook examples:

Installation

pip install gmm-lbd

OR if you clone this repository, you can make a

python setup.py develop

Roadmap

Low level (Gaussian mixture)

  • Automaticly choose the number of gaussians
  • conditional probability of GMM
  • regression of GMM
  • product of retrived means and covariances of GMM
  • product with non consistent shapes
  • speed management

High level (combinaison of movements)

  • Quick add pypot records for any motors
  • Easyly represent GMM with ellipses
  • Plot ellipse for GMM with more than 2 dimensions
  • Align movements with DTW
  • White detection (at begin and end of movements)
  • Sequential : concatenation of GMMs
  • Concurent : product
  • Add a coefficiant to rise or low the importance of a movement
  • Add a filter in the sequential combinaison
  • Adapt to use an IK model, for performing task space trajectory: WANTED
  • Incremental definition of the GMM for each new representation @Calinon07HRI (very good idea for online learning)

Pypot records improvements (TODO)

  • record datas with a variable framerate (compression + CPU usage for generating GMMs )