Ponencia
Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation
Autor/es | Pérez Hurtado de Mendoza, Ignacio
Capitán Fernández, Jesús Caballero, Fernando Merino, Luis |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2015 |
Fecha de depósito | 2019-07-05 |
Publicado en |
|
ISBN/ISSN | 978-3-319-27148-4 0302-9743 |
Resumen | Robots navigating in a social way should reason about people intentions
when acting. For instance, in applications like robot guidance or meeting with a
person, the robot has to consider the goals of the people. Intentions ... Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable, and thus we propose Partially Observable Markov Decision Processes (POMDPs) as a decision-making tool for these applications. One of the issues with POMDPs is that the prediction models are usually handcrafted. In this paper, we use machine learning techniques to build prediction models from observations. A novel technique is employed to discover points of interest (goals) in the environment, and a variant of Growing Hidden Markov Models (GHMMs) is used to learn the transition probabilities of the POMDP. The approach is applied to an autonomous telepresence robot. |
Cita | Pérez Hurtado de Mendoza, I., Capitán, J., Caballero, F. y Merino, L. (2015). Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation. En Robot 2015: Second Iberian Robotics Conference (247-258), Lisboa, Portugal: Springer. |
Ficheros | Tamaño | Formato | Ver | Descripción |
---|---|---|---|---|
Decision-Theoretic Planning.pdf | 6.355Mb | [PDF] | Ver/ | |