Spiking neural network based emotional model for robot partner

In this paper, a spiking neural network based emotional model is proposed for a smart phone based robot partner. Since smart phone has limited computational power compared to personal computers, a simple spike response model is applied for the neurons in the neural network. The network has three layers following the concept of emotion, feeling, and mood. The perceptual input stimulates the neurons in the first, emotion layer. Weights adjustment is also proposed for the interconnected neurons in the feeling layer and between the feeling and mood layer based on Hebbian learning. Experiments are presented to validate the proposed method. Based on the emotional model, the output action such as gestural and facial expressions for the robot is calculated.

[1]  Randolph R. Cornelius,et al.  The science of emotion: Research and tradition in the psychology of emotion. , 1997 .

[2]  Toru Yamaguchi,et al.  Estimation of human transport modes by fuzzy spiking neural network and evolution strategy in informationally structured space , 2013, 2013 IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS).

[3]  Christoph Bartneck,et al.  Subtle emotional expressions of synthetic characters , 2005, Int. J. Hum. Comput. Stud..

[4]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

[5]  Naoyuki Kubota,et al.  Cognitive Development in Partner Robots for Information Support to Elderly People , 2011, IEEE Transactions on Autonomous Mental Development.

[6]  Toru Yamaguchi,et al.  Extraction of Daily Life Log Measured by Smart Phone Sensors Using Neural Computing , 2013, KES.

[7]  L. Pessoa.  On the relationship between emotion and cognition , 2008, Nature Reviews Neuroscience.

[8]  N. Kubota,et al.  Structured learning for partner robots based on natural communication , 2008, 2008 IEEE Conference on Soft Computing in Industrial Applications.

[9]  Naoyuki Kubota,et al.  Development platform for robot partners using smart phones , 2013, MHS2013.

[10]  Naoyuki Kubota,et al.  Emotional models for multi-modal communication of robot partners , 2013, 2013 IEEE International Symposium on Industrial Electronics.

[11]  Ross Buck,et al.  The communication of emotion , 1984 .

[12]  Takenori Obo,et al.  Localization of human based on fuzzy spiking neural network in informationally structured space , 2010, International Conference on Fuzzy Systems.

[13]  Naoyuki Kubota,et al.  Gestural and facial communication with smart phone based robot partner using emotional model , 2014, 2014 World Automation Congress (WAC).

[14]  M. Seif El-Nasr,et al.  A fuzzy emotional agent for decision-making in a mobile robot , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[15]  D. Sperber,et al.  Relevance: Communication and Cognition , 1989 .

[16]  Naoyuki Kubota,et al.  Computational intelligence for structured learning of a partner robot based on imitation , 2005, Inf. Sci..

[17]  Naoyuki Kubota,et al.  Emotional Model Based on Computational Intelligence for Partner Robots , 2010, Modeling Machine Emotions for Realizing Intelligence.

[18]  Wulfram Gerstner,et al.  Spiking Neuron Models , 2002 .

[19]  Christopher J. Bishop,et al.  Pulsed Neural Networks , 1998 .

[20]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[21]  Feng Shu,et al.  A biologically-inspired affective model based on cognitive situational appraisal , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[22]  D. Sperber,et al.  Relevance: Communication and Cognition , 1997 .