A Situation-Aware Fear Learning (SAFEL) Model for Robots

Caroline Rizzi, Colin G. Johnson, Fabio Fabris, Patricia A. Vargas

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
60 Downloads (Pure)

Abstract

This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to predict undesirable or threatening situations based on past experiences. One of the main objectives is to allow robots to learn complex temporal patterns of sensed environmental stimuli and create a representation of these patterns. This memory can be later associated with a negative or positive “emotion”, analogous to fear and confidence. Experiments with a real robot demonstrated SAFEL's success in generating contextual fear conditioning behavior with predictive capabilities based on situational information.
Original languageEnglish
Pages (from-to)32–47
Number of pages16
JournalNeurocomputing
Volume221
Early online date20 Sep 2016
DOIs
Publication statusPublished - 19 Jan 2017

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