An Inhibition of Return Mechanism for the Exploration of Sensorimotor Contingencies

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The modelling of cognition is fundamental to designing robots that are increasingly more autonomous. Indeed, researchers take inspiration from human and animal cognition in order to endow robots with the ability to learn and adapt to their environment. In specific cases, the robot has to find the right compromise between exploring the environment, or exploiting its own experience to advance its knowledge of a skill. Our approach considers a neurally-inspired model to learning sensorimotor contingencies based on exploration and exploitation. For the exploration, an inhibition of return mechanism is implemented that generates new actions. In this work, we investigate how the tuning of the inhibition of return affects the exploratory behavior. To do so, we set up an experiment where a 3D printed humanoid robot arm GummiArm has to learn how to move a baby mobile toy with only a visual feedback. The results demonstrate that the tuning of the inhibition of return influences the exploratory behavior, leading to a faster learning of sensorimotor contingencies as well as the exploration of a reduced motor space.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020
EditorsGiancarlo Fortino, Fei-Yue Wang, Andreas Nurnberger, David Kaber, Rino Falcone, David Mendonca, Zhiwen Yu, Antonio Guerrieri
Number of pages6
ISBN (Electronic)9781728158716
ISBN (Print)978-1-7281-5872-3
Publication statusPublished - Sept 2020
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Human-Machine Systems - Virtual, Rome, Italy
Duration: 7 Sept 20209 Sept 2020


ConferenceIEEE International Conference on Human-Machine Systems
CityVirtual, Rome


  • Cognitive Robotics
  • Developmental Robotics
  • Dynamic Neural Fields
  • Embodiment
  • Exploration

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Human-Computer Interaction


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