Learning to generate proactive and reactive behavior using a dynamic neural network model with time-varying variance prediction mechanism

Shingo Murata, Hiroaki Arie, Tetsuya Ogata, Shigeki Sugano, Jun Tani

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper discusses a possible neurodynamic mechanism that enables self-organization of two basic behavioral modes, namely a proactive mode and a reactive mode, and of autonomous switching between these modes depending on the situation. In the proactive mode, actions are generated based on an internal prediction, whereas in the reactive mode actions are generated in response to sensory inputs in unpredictable situations. In order to investigate how these two behavioral modes can be self-organized and how autonomous switching between the two modes can be achieved, we conducted neurorobotics experiments by using our recently developed dynamic neural network model that has a capability to learn to predict time-varying variance of the observable variables. In a set of robot experiments under various conditions, the robot was required to imitate others movements consisting of alternating predictable and unpredictable patterns. The experimental results showed that the robot controlled by the neural network model was able to proactively imitate predictable patterns and reactively follow unpredictable patterns by autonomously switching its behavioral modes. Our analysis revealed that variance prediction mechanism can lead to self-organization of these abilities with sufficient robustness and generalization capabilities.

Original languageEnglish
Pages (from-to)1189-1203
Number of pages15
JournalAdvanced Robotics
Volume28
Issue number17
DOIs
Publication statusPublished - 2014 Sep 2
Externally publishedYes

Keywords

  • humanoid robot
  • imitation
  • proactive behavior
  • reactive behavior
  • recurrent neural network

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications

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