Free Energy Principle Based Precision Modulation for Robot Attention: Towards brain inspired robot intelligence

Research output: ThesisDissertation (TU Delft)

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The potential impact of a grand unified theory of the brain on the robotics community might be immense, as it might hold the key to the general artificial intelligence. Such a theory might make revolutionary leaps in robot intelligence by improving the quality of our lives. The last two decades have witnessed the rise of one such brain theory - the free energy principle (FEP) - that seems to be successful in explaining a large body of cognitive functions. The tremendous amount of research centering FEP is a testament to its popularity within the neuroscience community. This raises two important questions: i) since biological systems are fundamentally different from robots, will FEP be useful in solving real robotics problems? ii) if so, will it outperform classical robot algorithms? To answer these questions, this thesis takes a step in the direction of applying FEP on three class of robotics challenges, with a special focus on Unmanned Aerial Vehicle (UAV): i) action, ii) perception and iii) active perception. This thesis demonstrates the usefulness of FEP in solving these challenges, and shows that FEP is particularly beneficial in dealing with colored (non-white) noise during estimation (perception) when compared to classical methods, marking the utility of FEP not only in neuroscience, but also in robotics. With these results, this thesis aims to contribute to the rise of FEP as a unified theory of robot intelligence.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
  • Wisse, M., Supervisor
  • Mohajerin Esfahani, P., Advisor
Award date10 Mar 2023
Electronic ISBNs978-94-6384-417-8
Publication statusPublished - 2023


  • Free Energy Principle
  • Robotics
  • Active Inference
  • System identification
  • Informative Path Planning
  • Formation Control
  • Filtering
  • Unmanned Aerial Vehicle


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