(closed) Master thesis

in machine learning, hierarchical reinforcement learning, robotics


The Machine Learning Research Lab (MLRL) is looking for a master student (m/f/d) in the domain of machine learning, control and robotic in Munich. The MLRL is part of Volkswagen Group IT and tackles fundamental research in machine learning and optimal control. We develop new methods for modelling and control of dynamical systems. The focus of this work is learning generative world models, simultaneous localisation and mapping (SLAM) based on onboard sensors of mobile robots as well as the optimal control of those. In a final step, these algorithms are tested on real systems, e.g. robot arms or mobile robots. For this purpose a robot lab with a variety of robotic systems, motion capture systems, a diverse set of sensors and so forth is available.

Your Tasks

In this thesis, we want to explore how to solve scenarios that require both high and low level
control operating in different frequencies using a hierarchical reinforcement learning framework. For example, consider a robot arm pushing a ball over difficult terrain (e.g. inclines) to a specific position in a maze using only a single finger. On a high level, the controller has to solve a navigation task, but on a low level the interaction while moving the object requires a very fast force feedback control loop such that constant contact with the ball is ensured so that it does not roll away. Analysing existing methods and developing a new algorithm for this type of task will be the focus of this thesis.

Your Qualifications

  • Student (master) in natural sciences or engineering disciplines.
  • Interest in machine learning, reinforcement learning and robotics.
  • Very good knowledge of the Python programming language. Experience with Tensorflow, Pytorch or Jax is a plus.
  • Very good knowledge of numerical optimisation, probability theory, information theory, calculus and linear algebra.
  • Good knowledge of tools such as git, LaTex and any Unix shell.
  • Self-motivated working.
  • Proficient in English. ​

More information on the lab can be found at argmax.ai.

Please apply with your curriculum vitae, certificate of enrolment, and overview of grades at the karriere.volkswagen.de portal.

Announcement at Volkswagen Stellenbörse