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 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.