An Empirical Study of Active Inference on a Humanoid Robot

Guillermo Oliver, Pablo Lanillos, Gordon Cheng

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

One of the biggest challenges in robotics is interacting under uncertainty. Unlike robots, humans learn, adapt, and perceive their body as a unity when interacting with the world. Here, we investigate the suitability of active inference, a computational model proposed for the brain and governed by the free-energy principle, for robotic body perception and action in a nonsimulated environment. We designed and deployed the algorithm on the humanoid iCub showing how our proposed model enabled the robot to have adaptive body perception and to perform robust upper body reaching and head object tracking behaviors even under high levels of sensor noise and discrepancies between the model and the real robot. Estimation and control are formalized as an inference problem where the body posterior state distribution is approximated by means of the variational free-energy bound, yielding to a minimization of the prediction error. Besides, our study forecasts reactive actions in the presence of sensorimotor conflicts, a mechanism that may be relevant in human body adaptation to uncertain situations.

Original languageEnglish
Pages (from-to)462-471
Number of pages10
JournalIEEE Transactions on Cognitive and Developmental Systems
Volume14
Issue number2
DOIs
StatePublished - 1 Jun 2022

Keywords

  • Active inference
  • bioinspired perception
  • free-energy optimization
  • humanoid robots
  • iCub
  • predictive coding

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