Next Article in Journal
Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free
Next Article in Special Issue
Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement
Previous Article in Journal
An Associative Memory Approach to Healthcare Monitoring and Decision Making
Previous Article in Special Issue
PHAROS—PHysical Assistant RObot System
Article

A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User

Department of Systems Engineering and Automation, Universidad Carlos III de Madrid, 28911 Madrid, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(8), 2691; https://doi.org/10.3390/s18082691
Received: 8 July 2018 / Revised: 6 August 2018 / Accepted: 14 August 2018 / Published: 16 August 2018
(This article belongs to the Special Issue Smart Decision-Making)
Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot. Following a homeostatic approach, the ultimate goal of the robot is to keep its wellbeing as high as possible. In order to achieve this goal, our decision making system uses learning mechanisms to assess the best action to execute at any moment. Considering that the proposed system has been implemented in a real social robot, human-robot interaction is of paramount importance and the learned behaviors of the robot are oriented to foster the interactions with the user. The operation of the system is shown in a scenario where the robot Mini plays games with a user. In this context, we have included a robust user detection mechanism tailored for short distance interactions. After the learning phase, the robot has learned how to lead the user to interact with it in a natural way. View Full-Text
Keywords: decision making; social robots; HRI; machine learning; motivation; drives; homeostasis; RGB-D; user detection decision making; social robots; HRI; machine learning; motivation; drives; homeostasis; RGB-D; user detection
Show Figures

Graphical abstract

MDPI and ACS Style

Maroto-Gómez, M.; Castro-González, Á.; Castillo, J.C.; Malfaz, M.; Salichs, M.A. A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User. Sensors 2018, 18, 2691. https://doi.org/10.3390/s18082691

AMA Style

Maroto-Gómez M, Castro-González Á, Castillo JC, Malfaz M, Salichs MA. A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User. Sensors. 2018; 18(8):2691. https://doi.org/10.3390/s18082691

Chicago/Turabian Style

Maroto-Gómez, Marcos, Álvaro Castro-González, José C. Castillo, María Malfaz, and Miguel A. Salichs. 2018. "A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User" Sensors 18, no. 8: 2691. https://doi.org/10.3390/s18082691

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop