Inverse Reinforcement Learning algorithms and features for robot navigation in crowds: An experimental comparison

Dizan Vasquez 1 Billy Okal 2 Kai Arras 2
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
2 Albert-Ludwigs University
Abstract : — For mobile robots which operate in human pop-ulated environments, modeling social interactions is key to understand and reproduce people's behavior. A promising approach to this end is Inverse Reinforcement Learning (IRL) as it allows to model the factors that motivate people's actions instead of the actions themselves. A crucial design choice in IRL is the selection of features that encode the agent's context. In related work, features are typically chosen ad hoc without systematic evaluation of the alternatives and their actual impact on the robot's task. In this paper, we introduce a new software framework to systematically investigate the effect features and learning algorithms used in the literature. We also present results for the task of socially compliant robot navigation in crowds, evaluating two different IRL approaches and several feature sets in large-scale simulations. The results are benchmarked according to a proposed set of objective and subjective performance metrics.
Type de document :
Communication dans un congrès
IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, 2014, Chicago, United States. pp.1341 - 1346, 2014, 〈10.1109/IROS.2014.6942731〉
Liste complète des métadonnées

Littérature citée [21 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/hal-01105265
Contributeur : Dizan Vasquez <>
Soumis le : mardi 20 janvier 2015 - 10:01:27
Dernière modification le : jeudi 11 octobre 2018 - 08:48:02
Document(s) archivé(s) le : vendredi 11 septembre 2015 - 07:40:18

Fichier

iros14.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | LIG_SRCPR | LIG_SIC_E-MOTION | LIG | UGA

Citation

Dizan Vasquez, Billy Okal, Kai Arras. Inverse Reinforcement Learning algorithms and features for robot navigation in crowds: An experimental comparison. IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, 2014, Chicago, United States. pp.1341 - 1346, 2014, 〈10.1109/IROS.2014.6942731〉. 〈hal-01105265〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

275

Téléchargements de fichiers

1109

Contact


archive-ouverte@inria.fr