Reducing Intuitive-Physics Prediction Error through Playing - Unité de Recherche CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy
Communication Dans Un Congrès Année : 2024

Reducing Intuitive-Physics Prediction Error through Playing

Résumé

We present a mobile robot that autonomously generates behaviors to calibrate its intuitive-physics engine, also known as the Game Engine in the Head (GEITH). Most POMDP and Active Inference learning techniques operate in a closed world in which the set of states is defined a priori. However, implementing an innate GEITH and a set of interactive behaviors allowed us to avoid these limitations and design a mechanism for information search and learning in an open world. The results show that over a few tens of interaction cycles, the robot's prediction errors decrease, which shows an improvement in the GEITH calibration. Moreover, the robot generates behaviors that human observers describe as playful.
Fichier principal
Vignette du fichier
georgeon.pdf (686.04 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04691312 , version 1 (08-09-2024)

Identifiants

  • HAL Id : hal-04691312 , version 1

Citer

Olivier Georgeon, Béatrice de Montera, Paul Robertson. Reducing Intuitive-Physics Prediction Error through Playing. International Workshop on Active Inference, Sep 2024, Oxford, United Kingdom. ⟨hal-04691312⟩
13 Consultations
55 Téléchargements

Partager

More