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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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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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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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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New JMLR paper accepted: "Non-parametric Policy Search with Limited Information Loss."

New JMLR paper accepted: “Non-parametric Policy Search with Limited Information Loss.”

H. van Hoof, G. Neumann, and J. Peters, “Non-parametric policy search with limited information loss,” Journal of Machine Learning Research, 2018. [BibTeX] [Abstract] [Download PDF]…

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New IJJR Paper accepted! "Learning Movement Primitive Libraries through Probabilistic Segmentation."

New IJJR Paper accepted! “Learning Movement Primitive Libraries through Probabilistic Segmentation.”

R. Lioutikov, G. Neumann, G. Maeda, and J. Peters, “Learning movement primitive libraries through probabilistic segmentation,” International Journal of Robotics Research (IJRR), vol. 36, iss….

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New ICML Paper: Local Bayesian Optimization

New ICML Paper: Local Bayesian Optimization

R. Akrour, D. Sorokin, J. Peters, and G. Neumann, “Local Bayesian optimization of motor skills,” in International Conference on Machine Learning (ICML), 2017. [BibTeX] [Abstract]…

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New AURO Paper: Using Probabilistic Movement Primitives in Robotics

New AURO Paper: Using Probabilistic Movement Primitives in Robotics

Well deserved, Alex! A. Paraschos, C. Daniel, J. Peters, and G. Neumann, “Using probabilistic movement primitives in robotics,” Autonomous Robots, vol. 42, iss. 3, pp….

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