Brain-inspired biomimetic robot control: a review

Adrià Mompó Alepuz*, Dimitrios Papageorgiou, Silvia Tolu

*Corresponding author for this work

Research output: Contribution to journalReviewpeer-review

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Abstract

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

Original languageEnglish
Article number1395617
JournalFrontiers in Neurorobotics
Volume18
Number of pages13
ISSN1662-5218
DOIs
Publication statusPublished - 2024

Keywords

  • Bio-inspired
  • brain
  • Control
  • Learning
  • Model-based
  • Nonlinear
  • robotics
  • Spiking

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