Pérez Higueras, NoéCaballero, FernandoMerino, LuisMerino, Luis2026-01-152026-01-152017-11-27International Journal of Social Robotics 10, 235–249 (2018).10.1007/s12369-017-0448-1https://hdl.handle.net/10433/25604This 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.application/pdfenAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Path PlanningLearning from DemonstrationSocial Robotsjournal articleopen access