Entropy (Jul 2024)

Active Inference for Learning and Development in Embodied Neuromorphic Agents

  • Sarah Hamburg,
  • Alejandro Jimenez Rodriguez,
  • Aung Htet,
  • Alessandro Di Nuovo

DOI
https://doi.org/10.3390/e26070582
Journal volume & issue
Vol. 26, no. 7
p. 582

Abstract

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Taking inspiration from humans can help catalyse embodied AI solutions for important real-world applications. Current human-inspired tools include neuromorphic systems and the developmental approach to learning. However, this developmental neurorobotics approach is currently lacking important frameworks for human-like computation and learning. We propose that human-like computation is inherently embodied, with its interface to the world being neuromorphic, and its learning processes operating across different timescales. These constraints necessitate a unified framework: active inference, underpinned by the free energy principle (FEP). Herein, we describe theoretical and empirical support for leveraging this framework in embodied neuromorphic agents with autonomous mental development. We additionally outline current implementation approaches (including toolboxes) and challenges, and we provide suggestions for next steps to catalyse this important field.

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