%0 Journal Article
%A Pérez Higueras, Noé
%A Caballero, Fernando
%A Merino, Luis
%A Merino, Luis
%T Teaching robot navigation behaviors to optimal RRT planners
%D 2017
%U https://hdl.handle.net/10433/25604
%X This work presents an approach for learning navigation behaviors for robots using Optimal Rapidly-exploring Random Trees (RRT*) as the main planner. A new learning algorithm combining both Inverse Reinforcement Learning (IRL) and RRT* is developed to learn the RRT* ’s cost function from demonstrations. A comparison with other state-of-the-art algorithms shows how the method can recover the behavior from the demonstrations. Finally, a learned cost function for social navigation is tested in real experiments with a robot in the laboratory.
%K Path Planning
%K Learning from Demonstration
%K Social Robots
%~