The rapid advancement of robotics poses the problem of a deep integration of robotic systems in human environments. In order to achieve this symbiosis between humans and robots, the artificial systems have to take into account one of the most important aspects in human life: emotions. The recognition and understanding of human emotions is crucial for robotic systems to behave in appropriate ways according to the situation and smoothly integrate with all the different aspects of human life. This paper proposes a novel algorithm which uses state-of-the-art techniques in Machine Learning, in particular Recurrent Neural Networks, to automatically infer emotional clues from non-stylized motions (i.e. motions which are not supposed to convey emotional information as primary goal). This algorithm recognized human emotions with an accuracy between 0.68 and 0.80, depending on the considered motion, and clearly overcomes human capacity in the same task for the considered cases studied. Since the implemented algorithm is able to perform online, its results can be used to allow a behavioural programming which gives the robot the flexibility to act in a more human-oriented way.

Emotional intelligence in robots: Recognizing human emotions from daily-life gestures

ROVETTA, STEFANO;
2017

Abstract

The rapid advancement of robotics poses the problem of a deep integration of robotic systems in human environments. In order to achieve this symbiosis between humans and robots, the artificial systems have to take into account one of the most important aspects in human life: emotions. The recognition and understanding of human emotions is crucial for robotic systems to behave in appropriate ways according to the situation and smoothly integrate with all the different aspects of human life. This paper proposes a novel algorithm which uses state-of-the-art techniques in Machine Learning, in particular Recurrent Neural Networks, to automatically infer emotional clues from non-stylized motions (i.e. motions which are not supposed to convey emotional information as primary goal). This algorithm recognized human emotions with an accuracy between 0.68 and 0.80, depending on the considered motion, and clearly overcomes human capacity in the same task for the considered cases studied. Since the implemented algorithm is able to perform online, its results can be used to allow a behavioural programming which gives the robot the flexibility to act in a more human-oriented way.
File in questo prodotto:
File Dimensione Formato  
2017icra-emotional.pdf

non disponibili

Tipologia: Documento in versione editoriale
Dimensione 843.58 kB
Formato Adobe PDF
843.58 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/876952
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? ND
social impact