Portrait of Marcus Fechner

Hi, I’m Marcus.

PhD Student·Karlsruhe Institute of Technology

I am a PhD student at the Applied Technical-Cognitive Systems group at the Karlsruhe Institute of Technology, where I am supervised by Prof. Dr.-Ing. Johann Marius Zöllner.

My research focuses on learning robot policies from large-scale, unlabeled video. Prevailing approaches rely on carefully curated, action-labeled demonstrations, which are costly to obtain and limited in behavioral diversity. I investigate how transferable action representations can instead be extracted from the abundance of human-activity video available on the web, such as footage of people cooking, assembling, repairing, and otherwise interacting with the physical world, so that agents can acquire generalizable skills without bespoke dataset construction for every downstream task. I am particularly interested in vision-language-action models, world models, and how imitation learning and reinforcement learning can be efficiently utilized to ground such representations in robust, executable behavior.

Before my PhD I finished my M.Sc. in Electrical Engineering and Information Technology, also at KIT. Outside of research, I’m into sim racing, motorsports, and all action-packed things.

News

Publications

2026

  1. Segment to Focus visualization thumbnail

    Segment to Focus: Guiding Latent Action Models in the Presence of Distractors

    Marcus Fechner, Hamza Adnan, Constantin C. Lüth, Matthew T. Jackson, Alexey Zakharov, J. Marius Zöllner

    arXiv preprint · 2026

  2. CIG visualization thumbnail

    CIG: Exploration via Conditional Information Gain

    Tim Joseph, Marcus Fechner, Philipp Stegmaier, Karam Daaboul, J. Marius Zöllner

    arXiv preprint · 2026

Teaching

Experience

  1. Doctoral Researcher

    Karlsruhe Institute of Technology

    Jun 2022 — Present

    Researching how agents can learn transferable skills from unlabeled web video.

  2. Internship, Applied Technical-Cognitive Systems

    FZI Research Center for Information Technology

    Sep 2019 — Feb 2020

    Combined Monte Carlo Tree Search and deep neural networks to accelerate motion planning for cooperative multi-agent driving.

  3. Internship, R&D Autonomous Driving

    Mercedes-Benz AG

    Jun 2017 — Oct 2017

    Optimized convolutional neural networks for efficient deployment on embedded automotive hardware.

  4. Internship, R&D Autonomous Driving

    Mercedes-Benz AG

    Aug 2016 — Jan 2017

    Built a minimal embedded Linux image with the Yocto Project to evaluate hardware platforms for autonomous driving.

BibTeX