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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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Reducing Intuitive-Physics Prediction Error Through Playing

  • 会议论文

  • 发表日期：

    2025年

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  • Olivier L. Georgeon,
  • Béatrice de Montéra,
  • Paul Robertson

摘要

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.