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Teaching Robot Navigation Behaviors to Optimal RRT Planners

N. Perez-Higueras, F. Caballero, and L. Merino. Teaching Robot Navigation Behaviors to Optimal RRT Planners. International Journal of Social Robotics, 10(5):1–16, Springer, 2017.

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Abstract

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.

BibTeX Entry

@ARTICLE{ijsr17irlrrt,
year={2017},
issn={1875-4791},
journal={International Journal of Social Robotics},
volume = {10},
number = {5},
title={{Teaching Robot Navigation Behaviors to Optimal RRT Planners}},
publisher={Springer},
author={N. Perez-Higueras and F. Caballero and L. Merino},
pages={1-16},
language={English},
  abstract={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.},
}

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