Qualitative Spatial Reasoning for Boosting Learning-Based Robotics

Abstract : Learning and motion planning are powerful methods that exhibit complementary strengths. While planning allows goal-directed actions to be computed when a reliable forward model is known, learning allows such models to be obtained autonomously. In this paper we outline how both methods can be combined using an expressive qualitative knowledge representation as a link. We argue that the crucial step in this integration is to employ a representation based on a well-defined semantics which empowers reasoning to boost performance of learning as well as of the resulting action plans. We present an architecture for learning based robotics that exploits qualitative reasoning. Expected beneficiaries of this approach are discussed, some of which are already demonstrated in proof-of-concept experiments.
Type de document :
Communication dans un congrès
IROS Workshop Machine Learning in Planning and Control of Robot Motion, 2015, Hamburg, Germany
Liste complète des métadonnées

Littérature citée [22 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01693628
Contributeur : Alexandra Kirsch <>
Soumis le : vendredi 26 janvier 2018 - 13:43:39
Dernière modification le : lundi 29 janvier 2018 - 13:43:34
Document(s) archivé(s) le : vendredi 25 mai 2018 - 06:10:13

Fichier

wolter15qualitative-ws-preprin...
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01693628, version 1

Citation

Diedrich Wolter, Alexandra Kirsch. Qualitative Spatial Reasoning for Boosting Learning-Based Robotics. IROS Workshop Machine Learning in Planning and Control of Robot Motion, 2015, Hamburg, Germany. 〈hal-01693628〉

Partager

Métriques

Consultations de la notice

24

Téléchargements de fichiers

15