Dipl.-Ing. Marko Pavlic

Email: marko.pavlic(at)tum.de

Raum: 2.02.23


Institut für Informatik 6 Technische Universität München,

Parkring 13, 85748 Garching bei München

Sprechstunde: per Email



Curriculum Vitae

Marko Pavlić is a research assistant in the Machine Vision and Perception Group under Prof. Dr.-Ing. Darius Burschka. He received his mater's degree in Eletrical Engineering from the Technical University of Graz, Austria, in 2019. Before joining the Machine Vision and Perception Group as a doctoral candidate in 2021, he worked as a Systems Engineer designing software for localisation of railway vehicles.

In his new role, he is part of the KI.Fabrik project that will enable to manufacture various mechatronic products by means of fully modular, reconfigurable, highly automated and integrated AI technologies.

Research Interests

  • Human Action Analysis
  • Learning from Demonstration (LfD)
  • Kalman Filter Design
  • Robotics

Current Projects

Here are current topics listed which I am working on. Please do not hesitate to contact me if you are interested in any of the following topics!!!

I am also open to new topics if you have any in mind and want to work with me.

Designing the factory of the future for AI-supported production technologies

In the KI.Fabrik project we are responsible for the development of sensitive robots and new AI algorithms.

Learning from Demonstration

The goal is to teach a robot new tasks by simply demonstrating the task to him. We use a RGB-D camera to track human motion and use this input to model the task.
The robot can recognize and repeat the learned motions, scaled in time and amplitude.
You are welcome to help improving the framework by trying different models. Even though Deep Learning is not my preferred approach, I am not averse to mentoring someone who wants to try this approach to solve the problem.

Kalman Filter Design for Tracking of Human Motion

We use a RGB-D camera to track human motion. This data is usually very noise and the task is to design a Kalman Filter for improving the human motion tracker.

Human Shoulder-Arm Inverse Kinematics for Improved Markerless Humand Body Tracking

Object Detection and Pose Estimation

The task is to use RGB-D camera input to recognize and estimate the 6D pose of objects. Objects will be factory related, e.g. gear box parts, storage boxes.

Computer Vision-Based Box Detection and Localization on Shelfs

Foto von Marko Pavlic

Dipl.-Ing. Marko Pavlic