RT Journal Article
T1 Teaching robot navigation behaviors to optimal RRT planners
A1 Pérez Higueras, Noé
A1 Caballero, Fernando
A1 Merino, Luis
A1 Merino, Luis
K1 Path Planning
K1 Learning from Demonstration
K1 Social Robots
AB 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.
PB Springer
YR 2017
FD 2017-11-27
LK https://hdl.handle.net/10433/25604
UL https://hdl.handle.net/10433/25604
LA en
NO International Journal of Social Robotics 10, 235–249 (2018).
NO Deporte e Informática
NO Service Robotics Lab
DS RIO
RD Apr 28, 2026