Reducing Intuitive-Physics Prediction Error through Playing - Archive ouverte HAL
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.
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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⟩
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