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A Human-Robot Mutual Learning System with Affect-Grounded Language Acquisition and Differential Outcomes Training

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Social Robotics (ICSR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14454))

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Abstract

This paper presents a novel human-robot interaction setup for robot and human learning of symbolic language for identifying robot homeostatic needs. The robot and human learn to use and respond to the same language symbols that convey homeostatic needs and the stimuli that satisfy the homeostatic needs, respectively. We adopted a differential outcomes training (DOT) protocol whereby the robot provides feedback specific (differential) to its internal needs (e.g. ‘hunger’) when satisfied by the correct stimulus (e.g. cookie). We found evidence that DOT can enhance the human’s learning efficiency, which in turn enables more efficient robot language acquisition. The robot used in the study has a vocabulary similar to that of a human infant in the linguistic “babbling” phase. The robot software architecture is built upon a model for affect-grounded language acquisition where the robot associates vocabulary with internal needs (hunger, thirst, curiosity) through interactions with the human. The paper presents the results of an initial pilot study conducted with the interactive setup, which reveal that the robot’s language acquisition achieves higher convergence rate in the DOT condition compared to the non-DOT control condition. Additionally, participants reported positive affective experiences, feeling of being in control, and an empathetic connection with the robot. This mutual learning (teacher-student learning) approach offers a potential contribution of facilitating cognitive interventions with DOT (e.g. for people with dementia) through increased therapy adherence as a result of engaging humans more in training tasks by taking an active teaching-learning role. The homeostatic motivational grounding of the robot’s language acquisition has potential to contribute to more ecologically valid and social (collaborative/nurturing) interactions with robots.

All authors declare that they have no conflicts of interest. Supported by an EUTOPIA Undergraduate Research Support Scheme (EURSS) grant to AM and SS for a research stay at ETIS, and by Brain +. The work was carried out partly at ETIS, CY Cergy Paris University, and partly at the Dept. of Applied IT, University of Gothenburg.

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Authors and Affiliations

  1. Department of Philosophy, University of Cambridge, Sidgwick Ave, Cambridge, UK

    Alva Markelius

  2. DICE lab, Department of Applied IT, University of Gothenburg, Forskningsgången 6, Gothenburg, Sweden

    Alva Markelius, Sofia Sjöberg, Martin Bergström & Robert Lowe

  3. ETIS Lab, CY Cergy Paris University - ENSEA - CNR SUMR8051, Paris, France

    Zakaria Lemhauori, Laura Cohen & Lola Cañamero

  4. Artificial Intelligence Lab, Vrije Universiteit Brussel (VUB), Pleinlaan 9, Brussels, Belgium

    Zakaria Lemhauori

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  1. Alva Markelius
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Correspondence to Alva Markelius .

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Editors and Affiliations

  1. Qatar University, Doha, Qatar

    Abdulaziz Al Ali

  2. Qatar University, Doha, Qatar

    John-John Cabibihan

  3. Qatar University, Doha, Qatar

    Nader Meskin

  4. University of Naples Federico II, Napoli, Italy

    Silvia Rossi

  5. Qingdao University, Qingdao, China

    Wanyue Jiang

  6. The University of Alabama, Tuscaloosa, AL, USA

    Hongsheng He

  7. National University of Singapore, Queenstown, Singapore

    Shuzhi Sam Ge

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Markelius, A. et al. (2024). A Human-Robot Mutual Learning System with Affect-Grounded Language Acquisition and Differential Outcomes Training. In: Ali, A.A., et al. Social Robotics. ICSR 2023. Lecture Notes in Computer Science(), vol 14454. Springer, Singapore. https://doi.org/10.1007/978-981-99-8718-4_10

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  • DOI: https://doi.org/10.1007/978-981-99-8718-4_10

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