AGNI Theses

 

In this project / thesis, you are tasked with developing a controller which enables a physiologically-based human model to navigate a complex obstacle course as quickly as possible [1][2][3][4]. You are provided with a human musculoskeletal model and a physics-based simulation environment where you can synthesize physically and physiologically accurate motions.

[1] https://youtu.be/8xLghMb97T0

[2] https://www.aicrowd.com/challenges/neurips-2019-learn-to-move-walk-around

[3] https://arxiv.org/abs/1804.00361

[4] https://core.ac.uk/download/pdf/340076771.pdf

If you would like to know more about this thesis / project opportunity, please contact: 

Dr. Andrew Melnik <andrew.melnik(at)uni-bielefeld.de>

Under the framework of Deutsche Forschungsgemeinschaft (DFG) project—DEXMAN (https://gepris.dfg.de/gepris/projekt/410916101?language=en) , we are offering a master thesis topic “robotic dexterous manipulation learning by demonstration”.

 

Vision-Based robot control typically requires a large amount of manual tuning and feature selection (edge, color, SIFT features, etc.). Modern (GPU-driven) computing power and more robust numerical methods, nowadays allow for training of deep neural networks that extract suitable features for a given task automatically and that are able to learn control tasks in an end-to-end fashion, i.e.