Master Thesis/Student Assistent: learning roboti data
Light-weight robotic arms become increasingly popular and important for cheap manufacturing as well as household tasks. The decreasing costs of these systems increases the effort of control. In this, we are looking for someone who is interested in working with real-world systems but has a background in machine learning.
You get to work with a new robotic arm, understanding its accuracy and motor control data. Using novel machine-learning techniques (GPs, NNs, or whatever is applicable in for this task) you will model the data and improve system accuracy.
In your thesis, you will have to do both practical work (i.e., work with an existing test setup) and programming (i.e., develop the algorithms for data analysis). Knowledge of machine learning methods, e.g. from following our ML1 course, are required. Programming experience in Python and Matlab expected.