Unsupervised Online Grounding for Social Robots

Robots that incorporate social norms in their behaviors are seen as more supportive, friendly, and understanding. Since it is impossible to manually specify the most appropriate behavior for all possible situations, robots need to be able to learn it through trial and error, by observing interactions between humans, or by utilizing theoretical knowledge available in natural language. In contrast to the former two approaches, the latter has not received much attention because understanding natural language is non-trivial and requires proper grounding mechanisms to link words to corresponding perceptual information. Previous grounding studies have mostly focused on grounding of concepts relevant to object manipulation, while grounding of more abstract concepts relevant to the learning of social norms has so far not been investigated. Therefore, this paper presents an unsupervised cross-situational learning based online grounding framework to ground emotion types, emotion intensities and genders. The proposed framework is evaluated through a simulated human–agent interaction scenario and compared to an existing unsupervised Bayesian grounding framework. The obtained results show that the proposed framework is able to ground words, including synonyms, through their corresponding perceptual features in an unsupervised and open-ended manner, while outperfoming the baseline in terms of grounding accuracy, transparency, and deployability.

[1]  S. R. Livingstone,et al.  The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English , 2018, PloS one.

[2]  Andrew D. M. Smith.  Cross-situational learning , 2012 .

[3]  Bilge Mutlu,et al.  Learning-Based Modeling of Multimodal Behaviors for Humanlike Robots , 2014, 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[4]  Luc Steels,et al.  The Grounded Naming Game , 2012 .

[5]  Kenny Smith,et al.  Learning Times for Large Lexicons Through Cross-Situational Learning , 2010, Cogn. Sci..

[6]  Matthew R. Walter,et al.  Approaching the Symbol Grounding Problem with Probabilistic Graphical Models , 2011, AI Mag..

[7]  Kenny Smith,et al.  Cross-Situational Learning: An Experimental Study of Word-Learning Mechanisms , 2011, Cogn. Sci..

[8]  Bram Vanderborght,et al.  A Survey of Expectations About the Role of Robots in Robot-Assisted Therapy for Children with ASD: Ethical Acceptability, Trust, Sociability, Appearance, and Attachment , 2015, Science and Engineering Ethics.

[9]  Paul Vogt.  Exploring the Robustness of Cross-Situational Learning Under Zipfian Distributions , 2012, Cogn. Sci..

[10]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[11]  Roger Bemelmans,et al.  Socially assistive robots in elderly care: a systematic review into effects and effectiveness. , 2012, Journal of the American Medical Directors Association.

[12]  H. Gleitman,et al.  Human simulations of vocabulary learning , 1999, Cognition.

[13]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  S. Carey.  The child as word learner , 1978 .

[15]  P. Ekman,et al.  Strong evidence for universals in facial expressions: a reply to Russell's mistaken critique. , 1994, Psychological bulletin.

[16]  Clifford Nass,et al.  Computers that care: investigating the effects of orientation of emotion exhibited by an embodied computer agent , 2005, Int. J. Hum. Comput. Stud..

[17]  Rajiv Khosla,et al.  Socially Assistive Robots in Elderly Care: A Mixed-Method Systematic Literature Review , 2014, Int. J. Hum. Comput. Interact..

[18]  Linda B. Smith,et al.  Infants rapidly learn word-referent mappings via cross-situational statistics , 2008, Cognition.

[19]  Bram Vanderborght,et al.  A Reinforcement Learning Based Cognitive Empathy Framework for Social Robots , 2020, Int. J. Soc. Robotics.

[20]  J. Siskind.  A computational study of cross-situational techniques for learning word-to-meaning mappings , 1996, Cognition.

[21]  Hui Yu,et al.  Robot-Enhanced Therapy: Development and Validation of Supervised Autonomous Robotic System for Autism Spectrum Disorders Therapy , 2019, IEEE Robotics & Automation Magazine.

[22]  Susan Carey,et al.  Acquiring a Single New Word , 1978 .

[23]  N. Akhtar,et al.  Early lexical acquisition: the role of cross-situational learning , 1999 .

[24]  J. Gibbs.  Norms: The Problem of Definition and Classification , 1965, American Journal of Sociology.

[25]  E. Markman,et al.  When it is better to receive than to give: Syntactic and conceptual constraints on vocabulary growth , 1994 .

[26]  Stevan Harnad,et al.  Symbol grounding problem , 1990, Scholarpedia.

[27]  D. Weber,et al.  The role of assistive robotics in the lives of persons with disability. , 2010, American journal of physical medicine & rehabilitation.