Using Ontology as a Strategy for Modeling the Interface Between the Cognitive and Robotic Systems

This work contributes to the social robotics area by defining an architecture, called Cognitive Model Development Environment (CMDE) that models the interaction between cognitive and robotic systems. The communication between these systems is formalized with the definition of an ontology, called OntPercept, that models the perception of the environment using the information captured by the sensors present in the robotic system. The formalization offered by the OntPercept ontology simplifies the development, reproduction and comparison of experiments. The validation of the results required the development of two additional components. The first, called Robot House Simulator (RHS), provides an environment where robot and human can interact socially with increasing levels of cognitive processing. The second component is represented by the cognitive system that models the behavior of the robot with the support of artificial intelligence based systems.

[1]  Roseli A. Francelin Romero,et al.  Reducing the gap between cognitive and robotic systems , 2017, 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[2]  Eric Martinson,et al.  Auditory Perspective Taking , 2013, IEEE Trans. Cybern..

[3]  J. Gregory Trafton,et al.  Cognitive Architectures for social human-robot interaction , 2016, 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[4]  Vladan Papić,et al.  Social Robotics in Education: State-of-the-Art and Directions , 2018, Advances in Service and Industrial Robotics.

[5]  Mary Ellen Foster,et al.  Natural language generation for social robotics: opportunities and challenges , 2019, Philosophical Transactions of the Royal Society B.

[6]  Reid G. Simmons,et al.  A receptionist robot for Brazilian people: study on interaction involving illiterates , 2017, Paladyn J. Behav. Robotics.

[7]  Tamas Haidegger,et al.  Extensions to the core ontology for robotics and automation , 2015 .

[8]  Roel Vertegaal,et al.  The Design of Organic User Interfaces: Shape, Sketching and Hypercontext , 2013, Interact. Comput..

[9]  Jonathan Evans Dual-processing accounts of reasoning, judgment, and social cognition. , 2008, Annual review of psychology.

[10]  Maartje M. A. de Graaf,et al.  Why Would I Use This in My Home? A Model of Domestic Social Robot Acceptance , 2019, Hum. Comput. Interact..

[11]  Sébastien Gérard,et al.  Towards a core ontology for robotics and automation , 2013, Robotics Auton. Syst..

[12]  Hidekazu Ikezaki,et al.  Advanced Taste Sensors Based on Artificial Lipid Membrane , 2013 .

[13]  Ricardo Ribeiro Gudwin,et al.  An Overview of the Multipurpose Enhanced Cognitive Architecture (MECA) , 2017, BICA.

[14]  Amy Neustein Speech and Automata in Health Care , 2014 .

[15]  L. Billeci,et al.  Autism and social robotics: A systematic review , 2016, Autism research : official journal of the International Society for Autism Research.

[16]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[17]  Roseli A. Francelin Romero,et al.  Cognitive and robotic systems: Speeding up integration and results , 2017, 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR).

[18]  George Samaras,et al.  A Study on the Deployment of a Service Robot in an Elderly Care Center , 2018, Int. J. Soc. Robotics.

[19]  Michael Kifer,et al.  High Accuracy Question Answering via Hybrid Controlled Natural Language , 2018, 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[20]  Ravinder Dahiya,et al.  Tactile sensing for robotic applications , 2008 .

[21]  Ignazio Infantino,et al.  Talking with Sentiment: Adaptive Expression Generation Behavior for Social Robots , 2018, WAF.

[22]  Arvind Ramanathan,et al.  Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization , 2013, PloS one.

[23]  Hadas Erel,et al.  Robots are Always Social: Robotic Movements are Automatically Interpreted as Social Cues , 2019, CHI Extended Abstracts.

[24]  Kiyoshi Toko,et al.  1A. Basic Principles of Taste Sensor , 2016 .

[25]  Tiesheng Wang,et al.  Electroactive polymers for sensing , 2016, Interface Focus.

[26]  Idaku Ishii,et al.  Review of some advances and applications in real-time high-speed vision: Our views and experiences , 2016, Int. J. Autom. Comput..

[27]  Rizqi Andry Ardiansyah,et al.  Design of An Electronic Narrator on Assistant Robot for Blind People , 2016 .

[28]  Angelo Cangelosi,et al.  Affordances in Psychology, Neuroscience, and Robotics: A Survey , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[29]  Shuichi Akizuki,et al.  A survey and technology trends of 3D features for object recognition , 2017 .

[30]  Véronique Perdereau,et al.  Tactile sensing in dexterous robot hands - Review , 2015, Robotics Auton. Syst..

[31]  José Luis Gordillo,et al.  Synthesis of odor tracking algorithms with genetic programming , 2016, Neurocomputing.

[32]  Edward H. Adelson,et al.  GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force , 2017, Sensors.

[33]  Zhaojie Ju,et al.  Multimodal Human Hand Motion Sensing and Analysis—A Review , 2019, IEEE Transactions on Cognitive and Developmental Systems.

[34]  Roseli A. Francelin Romero,et al.  RHS simulator for robotic cognitive systems , 2017, 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR).

[35]  Yue Wang,et al.  ReHRI'17 - Towards Reproducible HRI Experiments: Scientific Endeavors, Benchmarking and Standardization , 2017, HRI.

[36]  Reid G. Simmons,et al.  Robotic Systems Architectures and Programming , 2008, Springer Handbook of Robotics, 2nd Ed..

[37]  Lu Yang,et al.  Survey on 3D Hand Gesture Recognition , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[38]  Jie Sun Emotion recognition and expression in therapeutic social robot design , 2014, HAI.

[39]  Primož Podržaj,et al.  Machine-Vision-Based Human-Oriented Mobile Robots: A Review , 2017 .

[40]  Liyang Yu,et al.  A Developer’s Guide to the Semantic Web , 2011, Springer Berlin Heidelberg.

[41]  Roland Siegwart,et al.  RGB–D terrain perception and dense mapping for legged robots , 2016, Int. J. Appl. Math. Comput. Sci..

[42]  Peter Ford Dominey,et al.  DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self , 2017, IEEE Transactions on Cognitive and Developmental Systems.

[43]  Illah R. Nourbakhsh,et al.  A survey of socially interactive robots , 2003, Robotics Auton. Syst..