Master Thesis: arm movement prediction from visual data

We have all the methodologies in house to do movement prediction based on visual data.  Using either our stochastic recurrent neural network (STORN) our our DMP-enhanced autoencoder neural networks (AE-DMP) we would like to use convolutional neural networks (cNN) to do the following:

  • translate movement in pixel domain to movement in latent space, possibly using simulated data;
  • translate that movement in latent space to movement in Cartesian / joint / ... space;
  • generalise these methodologies from one arm to two arms;
  • generalise these methodologies among arms.

We prefer candidates who are familiar with probabilistic machine learning.  An affinity with deep learning is surely required.  The work will involve applying our models in a python environment.

application procedure

To apply, please email us with the following information:

  • a letter of interest, including your prospective period of stay (note we normally do not accept students for less than 5 months)
  • a CV
  • a list of courses followed and grades
  • reprints of articles or theses, where applicable

Please email all files as PDF to:

Picture of  Patrick van der Smagt

Patrick van der Smagt

current: Head of AI Research, data lab, VW Group

Previous: Director of BRML labs
fortiss, An-Institut der Technischen Universit√§t M√ľnchen
Professor for Biomimetic Robotics and Machine Learning, TUM

Chairman of Assistenzrobotik e.V.
smagtbrmlorg



We have somewhat different guidelines as to what a Master's thesis at BRML should look like.  Please look at this piece of advise.