Adaptive and Energy-Efficient Optimal Control in CPGs Through Tegotae-Based Feedback

Riccardo Zamboni, Dai Owaki, Mitsuhiro Hayashibe

Research output: Contribution to journalArticlepeer-review

Abstract

To obtain biologically inspired robotic control, the architecture of central pattern generators (CPGs) has been extensively adopted to generate periodic patterns for locomotor control. This is attributed to the interesting properties of nonlinear oscillators. Although sensory feedback in CPGs is not necessary for the generation of patterns, it plays a central role in guaranteeing adaptivity to environmental conditions. Nonetheless, its inclusion significantly modifies the dynamics of the CPG architecture, which often leads to bifurcations. For instance, the force feedback can be exploited to derive information regarding the state of the system. In particular, the Tegotae approach can be adopted by coupling proprioceptive information with the state of the oscillation itself in the CPG model. This paper discusses this policy with respect to other types of feedback; it provides higher adaptivity and an optimal energy efficiency for reflex-like actuation. We believe this is the first attempt to analyse the optimal energy efficiency along with the adaptivity of the Tegotae approach.

Original languageEnglish
Article number632804
JournalFrontiers in Robotics and AI
Volume8
DOIs
Publication statusPublished - 2021 May 26

Keywords

  • central pattern generator
  • efficiency
  • embodiment
  • learning
  • optimal control
  • sensory feedback
  • tegotae approach

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

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