limbo

Limbo (LIbrary for Model-Based Optimization) is an open-source C++11 library for Gaussian Processes and data-efficient optimization (e.g., Bayesian optimization) that is designed to be both highly flexible and very fast. It can be used as a state-of-the-art optimization library or to experiment with novel algorithms with “plugin” components.

Documentation & Versions

The development branch is the master branch. For the latest stable release, check the release-2.1 branch.
Documentation is available at: http://www.resibots.eu/limbo

Citing Limbo

If you use Limbo in a scientific paper, please cite:

Cully, A., Chatzilygeroudis, K., Allocati, F., and Mouret J.-B., (2018). Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization. The Journal of Open Source Software.

In BibTex:

@article{cully2018limbo,
    title={{Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization}},
    author={Cully, A. and Chatzilygeroudis, K. and Allocati, F.  and Mouret, J.-B.},
    year={2018},
    journal={{The Journal of Open Source Software}},
    publisher={The Open Journal},
    volume={3},
    number={26},
    pages={545},
    doi={10.21105/joss.00545}
}

Authors

• Antoine Cully (Imperial College): http://www.antoinecully.com
• Jean-Baptiste Mouret (Inria): http://members.loria.fr/JBMouret
• Konstantinos Chatzilygeroudis (Inria): http://costashatz.github.io/
• Federico Allocati (Inria)

Other contributors

• Vaios Papaspyros (Inria)
• Roberto Rama (Inria)

Limbo is partly funded by the ResiBots ERC Project (http://www.resibots.eu).

Main features

• Implementation of the classic algorithms (Bayesian optimization, many kernels, likelihood maximization, etc.)
• Modern C++-11
• Generic framework (template-based / policy-based design), which allows for easy customization, to test novel ideas
• Experimental framework that allows user to easily test variants of experiments, compare treatments, submit jobs to clusters (OAR scheduler), etc.
• High performance (in particular, Limbo can exploit multi-core computers via Intel TBB and vectorize some operations via Eigen3)
• Purposely small to be easily maintained and quickly understood

Scientific articles that use Limbo

• Chatzilygeroudis, K., & Mouret, J. B. (2018). Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics. Proceedings of the International Conference on Robotics and Automation (ICRA).
• Pautrat, R., Chatzilygeroudis, K., & Mouret, J.-B. (2018). Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search. Proceedings of the International Conference on Robotics and Automation (ICRA).
• Chatzilygeroudis, K., Vassiliades, V. and Mouret, J.-B. (2017). Reset-free Trial-and-Error Learning for Robot Damage Recovery. Robotics and Autonomous Systems.
• Karban P., Pánek D., Mach F. and Doležel, I. (2017). Calibration of numerical models based on advanced optimization and penalization techniques. Journal of Electrical Engineering, 68(5), 396-400.
• Chatzilygeroudis K., Rama R., Kaushik, R., Goepp, D., Vassiliades, V. and Mouret, J.-B. (2017). Black-Box Data-efficient Policy Search for Robotics. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
• Tarapore, D., Clune, J., Cully, A., and Mouret, J.-B. (2016). How Do Different Encodings Influence the Performance of the MAP-Elites Algorithm?. In Proc. of Genetic and Evolutionary Computation Conference.
• Cully, A., Clune, J., Tarapore, D., and Mouret, J.-B. (2015). Robots that can adapt like animals. Nature, 521(7553), 503-507.
• Chatzilygeroudis, K., Cully, A. and Mouret, J.-B. (2016). Towards semi-episodic learning for robot damage recovery. Workshop on AI for Long-Term Autonomy at the IEEE International Conference on Robotics and Automation 2016.
• Papaspyros, V., Chatzilygeroudis, K., Vassiliades, V., and Mouret, J.-B. (2016). Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors. Workshop on Bayesian Optimization at the Annual Conference on Neural Information Processing Systems (NIPS) 2016.

Research projects that use Limbo

• Resibots. ERC Starting Grant: http://www.resibots.eu/
• PAL. H2020 EU project: http://www.pal4u.eu/