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My name is Gerhard Neumann and I am Professor of Robotics and Autonomous Systems at the University of Lincoln. I am heading the Computational Learning for Autonomous Systems (CLAS) team which is part of the Lincoln Centre for Autonomous Systems (LCAS). Part of the team is still based at the TU Darmstadt, my former affiliation. We still heavily cooperate with other teams from TUDa such as the IAS team from Prof. Jan Peters or the group from Prof. Johannes Fuernkranz. We work on machine learning methods for autonomous systems, with a focus on reinforcement learning, policy search and imitation learning.  Our group’s research focus is the use of domain knowledge from robotics to develop new data-driven machine learning algorithms that scale favorably to the complexity of robotic tasks.

This site is still under construction. More content will be added soon.

Research Fields

Our research concentrates on the following sub-fields of machine learning:

Applications

We focus on a wide range of applications where machine learning methods could prove a huge benefit in the future. We work on specific applications (agriculture, nuclear robotics) where robots are highly needed in the next years while other application areas serve as proof-of-concept studies to evaluate our algorithms. Our application fields include:

  • Grasping and Manipulation
  • Agri-culture Robotics
  • Sort and Segregate of Nuclear Waste
  • Dynamic Motor Games, Table-Tennis, Beer-Bong…
  • Robot Swarms

News

  • New RAL Paper: “Probabilistic Prioritization of Movement Primitives”

    Alex’s last journal paper for his PhD has been accepted! Congratulations!

    • A. Paraschos, R. Lioutikov, J. Peters, and G. Neumann, “Probabilistic prioritization of movement primitives,” IEEE Robotics and Automation Letters, vol. PP, iss. 99, 2017.
      [BibTeX] [Abstract] [Download PDF]

      Movement prioritization is a common approach to combine controllers of different tasks for redundant robots, where each task is assigned a priority. The priorities of the tasks are often hand-tuned or the result of an optimization, but seldomly learned from data. This paper combines Bayesian task prioritization with probabilistic movement primitives to prioritize full motion sequences that are learned from demonstrations. Probabilistic movement primitives (ProMPs) can encode distributions of movements over full motion sequences and provide control laws to exactly follow these distributions. The probabilistic formulation allows for a natural application of Bayesian task prioritization. We extend the ProMP controllers with an additional feedback component that accounts inaccuracies in following the distribution and allows for a more robust prioritization of primitives. We demonstrate how the task priorities can be obtained from imitation learning and how different primitives can be combined to solve even unseen task-combinations. Due to the prioritization, our approach can efficiently learn a combination of tasks without requiring individual models per task combination. Further, our approach can adapt an existing primitive library by prioritizing additional controllers, for example, for implementing obstacle avoidance. Hence, the need of retraining the whole library is avoided in many cases. We evaluate our approach on reaching movements under constraints with redundant simulated planar robots and two physical robot platforms, the humanoid robot ?iCub? and a KUKA LWR robot arm.

      @article{lirolem27901,
      author = {Alexandros Paraschos and Rudolf Lioutikov and Jan Peters and Gerhard Neumann},
      number = {99},
      year = {2017},
      journal = {IEEE Robotics and Automation Letters},
      month = {July},
      booktitle = {, Proceedings of the International Conference on Intelligent Robot Systems, and IEEE Robotics and Automation Letters (RA-L)},
      title = {Probabilistic prioritization of movement primitives},
      volume = {PP},
      publisher = {IEEE},
      keywords = {ARRAY(0x559005f53e48)},
      url = {http://eprints.lincoln.ac.uk/27901/},
      abstract = {Movement prioritization is a common approach
      to combine controllers of different tasks for redundant robots,
      where each task is assigned a priority. The priorities of the
      tasks are often hand-tuned or the result of an optimization,
      but seldomly learned from data. This paper combines Bayesian
      task prioritization with probabilistic movement primitives to
      prioritize full motion sequences that are learned from demonstrations.
      Probabilistic movement primitives (ProMPs) can
      encode distributions of movements over full motion sequences
      and provide control laws to exactly follow these distributions.
      The probabilistic formulation allows for a natural application of
      Bayesian task prioritization. We extend the ProMP controllers
      with an additional feedback component that accounts inaccuracies
      in following the distribution and allows for a more
      robust prioritization of primitives. We demonstrate how the
      task priorities can be obtained from imitation learning and
      how different primitives can be combined to solve even unseen
      task-combinations. Due to the prioritization, our approach can
      efficiently learn a combination of tasks without requiring individual
      models per task combination. Further, our approach can
      adapt an existing primitive library by prioritizing additional
      controllers, for example, for implementing obstacle avoidance.
      Hence, the need of retraining the whole library is avoided in
      many cases. We evaluate our approach on reaching movements
      under constraints with redundant simulated planar robots and
      two physical robot platforms, the humanoid robot ?iCub? and
      a KUKA LWR robot arm.}
      }