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…

Read Article →
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…

Read Article →
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….

Read Article →
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,…

Read Article →
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…

Read Article →
JMLR 2017: Non-parametric Policy Search with Limited Information Loss.

JMLR 2017: Non-parametric Policy Search with Limited Information Loss.

Learning complex control policies from non-linear and redundant sensory input is an importantĀ challenge for reinforcement learning algorithms. Non-parametric methods that approximate values functions or transition…

Read Article →
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, vol. 18, iss. 73, pp….

Read Article →
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….

Read Article →
IJJR 2017:  Learning Movement Primitive Libraries through Probabilistic Segmentation

IJJR 2017: Learning Movement Primitive Libraries through Probabilistic Segmentation

Movement primitives are a well established approach for encoding and executing movements. While the primitivesĀ themselves have been extensively researched, the concept of movement primitive libraries…

Read Article →
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]…

Read Article →