Skip to content

This repository contains the code for the paper "Geometry-aware Bayesian Optimization in Roboticsusing Riemannian Matérn Kernels" (CoRL'21).

License

Notifications You must be signed in to change notification settings

NoemieJaquier/MaternGaBO

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

BoManifolds

This repository contains the source code to perform Geometry-aware Bayesian Optimization with Riemannian Matérn kernels.

Dependencies

This code runs with Python>=3.7. It requires the following packages:

  • numpy
  • scipy
  • matplotlib
  • pymanopt
  • torch
  • gpytorch
  • botorch
  • sympy

Installation

To install it, first clone the repository and install the related packages, as explained below.

pip install -r requirements.txt

Examples

The following example are available:

Kernels (kernels_manifolds_examples/)

These examples show the computation of various Riemannian and Euclidean kernels on different manifolds.

  • kernels_euclidean.py
  • kernels_sphere.py
  • kernels_spd.py
  • kernels_hyperbolic.py
  • kernels_so.py
  • kernels_torus.py

Bayesian optimization (bo_manifolds_benchmark_examples/)

These examples show the use of Bayesian optimization on various manifolds to optimize benchmark functions. For each example, the type of BO, the type of kernel and acquisition function, the dimension of the manifold, and the benchmark function can be selected by the user.

  • bo_manifold_sphere.py
  • bo_manifold_spd.py
  • bo_manifold_hyperbolic.py
  • bo_manifold_so.py
  • bo_manifold_torus.py

References

If you found this code useful, we would be grateful if you cite the following reference:

[ 1 ] N. Jaquier*, V. Borovitskiy*, A. Smolensky, A. Terenin, T. Asfour, and L. Rozo (2021). Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels. In Conference on Robot Learning (CoRL).

You can find the video accompanying the paper here.

@inproceedings{Jaquier21MaternGaBO,
	author="Jaquier, N. and Borovitskiy, V. and Smolensky, A. and Terenin, A. and Asfour, T. and Rozo, L.", 
	title="Geometry-aware Bayesian Optimization in Robotics using Riemannian Mat\'ern Kernels",
	booktitle="Conference on Robot Learning (CoRL)",
	year="2021",
	pages=""
}

About

This repository contains the code for the paper "Geometry-aware Bayesian Optimization in Roboticsusing Riemannian Matérn Kernels" (CoRL'21).

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Languages