Some of our work has resulted in software packages that may be useful to other researchers or practitioners. If you find it useful, please reference the relevant papers.


The EXOTica library is a generic Optimisation Toolset for Robotics platforms, written in C++ and with bindings for Python. Its motivation is to provide a more streamlined process for developing and benchmarking algorithms for motion planning and control.

Please acknowledge/reference:

Vladimir Ivan, Yiming Yang, Wolfgang Merkt, Michael P. Camilleri, Sethu Vijayakumar, EXOTica: An Extensible Optimization Toolset for Prototyping and Benchmarking Motion Planning and Control, In: Koubaa A. (eds) Robot Operating System (ROS). Studies in Computational Intelligence, Springer, vol. 778, pp. 211-240 (2019) [preprint pdf] [software webpage] [DOI]

The library is freely available under the terms of the LGPL (with an exception that allows for static linking).


Crocoddyl is an optimal control library for robot control under contact sequence. Its solvers are based on novel and efficient Differential Dynamic Programming (DDP) algorithms. Crocoddyl computes optimal trajectories along with optimal feedback gains. It uses Pinocchio for fast computation of robots dynamics and their analytical derivatives.

Please acknowledge/reference:

C. Mastalli, R. Budhiraja, W.Merkt, G. Saurel, B. Hammoud, M. Naveau, J. Carpentier, L. Righetti, S. Vijayakumar and N. Mansard, Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control, Proc. IEEE International Conference on Robotics and Automation (ICRA 2020), Paris, France (2020). [pdf]

The source code is released under the BSD 3-Clause license.


Locally Weighted Projection Regression (LWPR) is an algorithm developed in our group that achieves online, incremental nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. We developed a C-library with wrappers for C++, Matlab/Octave, and Python.

Please acknowledge/reference:

S. Vijayakumar, A. D'Souza and S. Schaal. Incremental Online Learning in High Dimensions. Neural Computation, vol. 17, no. 12, pp. 2602-2634 (2005). [pdf]

S. Klanke, S. Vijayakumar and S. Schaal, A library for Locally Weighted Projection Regression, Journal of Machine Learning Research (JMLR), vol. 9, pp. 623-626 (2008). [pdf] [Supplementary Documentation]

The library is freely available under the terms of the LGPL (with an exception that allows for static linking).