3 new ICRA papers!

3 new ICRA papers!

G. H. W. Gebhardt, K. Daun, M. Schnaubelt, and G. Neumann, “Robust Learning of Object Assembly Tasks with an Invariant Representation of Robot Swarms,” in…

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New Survey Paper: Preference-based Reinforcement Learning

New Survey Paper: Preference-based Reinforcement Learning

New survey paper published at JMLR! C. Wirth, R. Akrour, G. Neumann, and J. Furnkranz, “A Survey of Preference-Based Reinforcement Learning Methods,” Journal of Machine…

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NCNR - National Center for Nuclear Robotics (EPSRC RAI Hub, robotics for extreme environments, 2017-2021)

NCNR – National Center for Nuclear Robotics (EPSRC RAI Hub, robotics for extreme environments, 2017-2021)

The National Center for Nuclear Robotics (NCNR) is a multi-disciplinary EPSRC RAI (Robotics and Artificial Intelligence) Hub consisting of most leading nuclear robotics experts in…

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We are part of the National Center for Nuclear Robotics (NCNR)!

We are part of the National Center for Nuclear Robotics (NCNR)!

  The National Center for Nuclear Robotics (NCNR) is a multi-disciplinary EPSRC RAI (Robotics and Artificial Intelligence) Hub consisting of most leading nuclear robotics experts…

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2 Innovate UK projects are starting!

2 Innovate UK projects are starting!

In Robo-Pic we are starting to pick mushrooms while in Automato, we are harvesting vine tomatoes… It almost makes already a nice topping of a…

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Automato (Innovate UK, 2017 - 2019)

Automato (Innovate UK, 2017 – 2019)

Automato will develop an automated robotic picking system for fresh vine tomatoes. It addresses a key threat to the long-term future of the UK tomato…

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2 new IROS papers accepted!

2 new IROS papers accepted!

J. Pajarinen, V. Kyrki, M. Koval, S. Srinivasa, J. Peters, and G. Neumann, “Hybrid control trajectory optimization under uncertainty,” in IEEE/RSJ International Conference on Intelligent…

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IROS 2017: Hybrid control trajectory optimization under uncertainty

IROS 2017: Hybrid control trajectory optimization under uncertainty

Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e….

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IJCAI 2017: Contextual CMA-ES

IJCAI 2017: Contextual CMA-ES

Many stochastic search algorithms are designed to optimize a fixed objective function to learn a task, i.e., if the objective function changes slightly, for example,…

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New IJCAI paper: Contextual CMA-ES

New IJCAI paper: Contextual CMA-ES

A. Abdolmaleki, B. Price, N. Lau, P. Reis, and G. Neumann, “Contextual CMA-ES,” in International Joint Conference on Artificial Intelligence (IJCAI), 2017. [BibTeX] [Abstract] [Download…

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