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About Gerhard Neumann

Gerhard is a Professor of Robotics & Autonomous Systems in College of Science at the University of Lincoln. Before coming to Lincoln, he has been an Assistant Professor at the TU Darmstadt from September 2014 to October 2016 and head of the Computational Learning for Autonomous Systems (CLAS) group. Before that, he was Post-Doc and Group Leader at the Intelligent Autonomous Systems Group (IAS) also in Darmstadt under the guidance of Prof. Jan Peters. Gerhard obtained his Ph.D. under the supervision of Prof. Wolfgang Mass at the Graz University of Technology. Gerhard already authored 50+ peer reviewed papers, many of them in top ranked machine learning and robotics journals or conferences such as NIPS, ICML, ICRA, IROS, JMLR, Machine Learning and AURO. He is principle investigator for the National Center for Nuclear Robotics (NCNR) in Lincoln which is an EPSRC RAI Hub and also leading 1 Innovate UK project on Tomato Picking. In Darmstadt, he is principle investigator of the EU H2020 project Romans and acquired DFG funding. He organized several workshops and is area chair for conferences such as NIPS and CoRL.

Subject Specialism

Machine Learning, Robotics, Reinforcement Learning, Imitation Learning, Deep Learning


  • Best Student Paper (supervisor of) — European Conference for Machine Learning (ECML),
  • Best Paper Finalist — International Conference on Robotics and Automation (ICRA),
  • Best Lecture Award — Fachschaft Informatik, Darmstadt University of Technology,
  • Best Paper Finalist — International Conference on Robotics and Automation (ICRA),
  • 1st Place in Scientific Challenge — Robocup Soccer 3D Simulation League 2014,
  • Best Cognitive Systems Paper — International Conference on Intelligent Robots and Systems (IROS),
  • Best Paper Award — IEEE RAS/RSJ Conference on Humanoids Robots (HUMANOIDS),

Research Interests

  • Deep Learning

  • Dexterous Manipulation

  • Imitation Learning

  • Kernel-Based Methods

  • Machine Learning

  • Motor Skill Learning

  • Multi-Agent Learning

  • Reinforcement Learning

  • Robot Swarms

  • Robotics

Research in the Lincoln Repository