A C++ implementation of the IRL algorithms RTIRL and RLT. These algorithms replace the MDP framework by a RRT* planner. So, their goal is to learn the weights of the RRT* cost function that lead the planner to behave similarly to the demonstrations.
The C++ library has interface (planning.h) that must be implemented in order to connect the learning with the desired RRT* planner and load properly the data for learning. In this case, the library of RRT* planners upo_rrt_planners and ROS has been used to implement the interface and perform the learning.
- Include other learning algorithms like Maximum Margin Planning (MMP) that uses an A* planner.
The package is a work in progress used in research prototyping. Pull requests and/or issues are highly encouraged.
 N. Pérez-Higueras, F. Caballero, and L. Merino, "Learning robot navigation behaviors by demonstration using a rrt* planner", in International Conference on Social Robotics. Springer International Publishing, 2016, pp. 1–10.
 K. Shiarlis, J. Messias, and S. Whiteson, "Rapidly exploring learning trees", in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Singapore, Singapore: IEEE, May 2017. [Online]. Available: http://teresaproject.eu/project/publications-new
 S. Karaman and E. Frazzoli, "Sampling-based algorithms for optimal motion planning", The International Journal of Robotics Research, vol. 30, no. 7, pp. 846–894, 2011.