Modeling Affordances and Functioning for Personalized Robotic Assistance

A key aspect of robotic assistants is their ability to contextualize their behavior according to different needs of assistive scenarios. This work presents an ontology-based knowledge representation and reasoning approach supporting the synthesis of personalized behavior of robotic assistants. It introduces an ontological model of health state and functioning of persons based on the International Classification of Functioning, Disability and Health. Moreover, it borrows the concepts of affordance and function from the literature of robotics and manufacturing and adapts them to robotic (physical and cognitive) assistance domain. Knowledge reasoning mechanisms are developed on top of the resulting ontological model to reason about stimulation capabilities of a robot and health state of a person in order to identify action opportunities and achieve personalized assistance. Experimental tests assess the performance of the proposed approach and its capability of dealing with different profiles and stimuli.

[1]  Masayuki Inaba,et al.  Continuous Modeling of Affordances in a Symbolic Knowledge Base , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[2]  Stefano Borgo,et al.  A formal ontological perspective on the behaviors and functions of technical artifacts , 2008, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[3]  Amedeo Cesta,et al.  A Two-Layered Approach to Adaptive Dialogues for Robotic Assistance , 2020, 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN).

[4]  John R. Anderson,et al.  ACT-R: A Theory of Higher Level Cognition and Its Relation to Visual Attention , 1997, Hum. Comput. Interact..

[5]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[6]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[7]  Carola Eschenbach,et al.  Formal Ontology in Information Systems , 2008 .

[8]  Amedeo Cesta,et al.  A Cognitive Loop for Assistive Robots - Connecting Reasoning on Sensed Data to Acting , 2018, 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[9]  Pat Langley,et al.  Cognitive architectures: Research issues and challenges , 2009, Cognitive Systems Research.

[10]  John K. Tsotsos,et al.  40 years of cognitive architectures: core cognitive abilities and practical applications , 2018, Artificial Intelligence Review.

[11]  Amedeo Cesta,et al.  A Holistic Approach to Behavior Adaptation for Socially Assistive Robots , 2020, Int. J. Soc. Robotics.

[12]  Shuichi Akizuki,et al.  A brief review of affordance in robotic manipulation research , 2017, Adv. Robotics.

[13]  Tim Miller,et al.  Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..

[14]  A. Tversky,et al.  Choices, Values, and Frames , 2000 .

[15]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[16]  M. Matarić,et al.  Personalized Socially Assistive Robotics , 2007 .

[17]  Silvia Rossi,et al.  User profiling and behavioral adaptation for HRI: A survey , 2017, Pattern Recognit. Lett..

[18]  David Vernon,et al.  The role of cognitive architectures in general artificial intelligence , 2018, Cognitive Systems Research.

[19]  Amedeo Cesta,et al.  Toward intelligent continuous assistance , 2020, J. Ambient Intell. Humaniz. Comput..

[20]  Michael Beetz,et al.  A Formal Model of Affordances for Flexible Robotic Task Execution , 2020, ECAI.

[21]  Alex Mihailidis,et al.  Learning and Personalizing Socially Assistive Robot Behaviors to Aid with Activities of Daily Living , 2018, ACM Transactions on Human-Robot Interaction.

[22]  N. Guarino,et al.  Formal Ontology in Information Systems : Proceedings of the First International Conference(FOIS'98), June 6-8, Trento, Italy , 1998 .

[23]  恵子 紀国谷 国際生活機能分類(International Classification of Functioning, Disability and Health: ICF)にみた福祉・保健・医療の専門職協働における連携に関する貢献と課題 , 2007 .

[24]  Amedeo Cesta,et al.  Integrating Resource Management and Timeline-Based Planning , 2018, ICAPS.

[25]  Amedeo Cesta,et al.  Knowledge-based adaptive agents for manufacturing domains , 2018, Engineering with Computers.

[26]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[27]  Brian Scassellati,et al.  Socially assistive robotics [Grand Challenges of Robotics] , 2007, IEEE Robotics & Automation Magazine.

[28]  D Feil-Seifer,et al.  Socially Assistive Robotics , 2011, IEEE Robotics & Automation Magazine.

[29]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[30]  Francisco Herrera,et al.  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2020, Inf. Fusion.

[31]  Amedeo Cesta,et al.  A Planning-Based Architecture for a Reconfigurable Manufacturing System , 2016, ICAPS.

[32]  Joachim Hertzberg,et al.  The Role of Functional Affordances in Socializing Robots , 2015, International Journal of Social Robotics.

[33]  三嶋 博之 The theory of affordances , 2008 .

[34]  Stefano Borgo,et al.  Technical artifacts: An integrated perspective , 2014, Appl. Ontology.

[35]  Tullio Tolio,et al.  Motion planning and scheduling for human and industrial-robot collaboration , 2017 .

[36]  D. Feil-Seifer,et al.  Defining socially assistive robotics , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[37]  Moritz Tenorth,et al.  Representations for robot knowledge in the KnowRob framework , 2017, Artif. Intell..