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Lukas Hermann

Albert-Ludwigs-Universität Freiburg
Technische Fakultät
Autonome Intelligente Systeme
Georges-Köhler-Allee 080
D-79110 Freiburg i. Br., Germany
Office:   080-00-023

hermannl@cs.uni-freiburg.de
Phone:   +49 761 203-8023

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About me

  • 10/2011 - 09/2015: Bachelor o.Sc. in Computer Science with minor in Cognitive Science, University of Freiburg
  • 10/2015 - 07/2019: Master o.Sc. in Computer Science with minor in Cognitive Science, University of Freiburg
  • 02/2020 - up to now: Working at the Autonomous Intelligent Systems group of Prof. Dr. Wolfram Burgard as PhD Student

Research Interests

  • Robot Learning
  • Deep Reinforcement Learning
  • Self-Supervised Learning

Current Research Projects

Publications

Conference Papers

  • Iman Nematollahi, Oier Mees, Lukas Hermann, Wolfram Burgard
    Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, 2020
    Download BibTeX Project Page Video
  • Maximilian Argus*, Lukas Hermann, Jon Long, Thomas Brox
    FlowControl: Optical Flow Based Visual Servoing
    IEEE International Conference on Intelligent Robots and System (IROS), Las Vegas, Arizona, 2020
    Download BibTeX Project Page Video
  • Lukas Hermann*, Maximilian Argus*, Andreas Eitel, Artemij Amiranashvili, Wolfram Burgard, Thomas Brox
    Adaptive Curriculum Generation from Demonstrations for Sim-To-Real Visuomotor Control
    IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020
    Download BibTeX Project Page Video

Workshop Papers

  • Jessica Borja-Diaz*, Oier Mees*, Gabriel Kalweit, Lukas Hermann, Joschka Boedecker, Wolfram Burgard
    NEW: Affordance Learning from Play for Sample-Efficient Policy Learning
    Workshop on Robot Learning: Self-Supervised and Lifelong Learning at NeurIPS, 2021
    Download BibTeX
  • Iman Nematollahi, Oier Mees, Lukas Hermann, Wolfram Burgard
    Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction
    Workshop on Machine Learning in Planning and Control of Robot Motion at IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020
    Download BibTeX
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