Bahar Irfan
Aditi Ramachandran
ABSTRACT
Adaptation and personalization are critical elements when modeling robot behaviors toward users in real-world settings. Multiple aspects of the user need to be taken into consideration in order to personalize the interaction, such as their personality, emotional state, intentions, and actions. While this information can be obtained a priori through self-assessment questionnaires or in real-time during the interaction through user profiling, behaviors and preferences can evolve in long-term interactions. Thus, gradually learning new concepts or skills (i.e., "lifelong learning'') both for the users and the environment is crucial to adapt to new situations and personalize interactions with the aim of maintaining their interest and engagement. In addition, adapting to individual differences autonomously through lifelong learning allows for inclusive interactions with all users with varying capabilities and backgrounds. The third edition of the "Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)'' workshop aims to gather and present interdisciplinary insights from a variety of fields, such as education, rehabilitation, elderly care, service and companion robots, for lifelong robot learning and adaptation to users, context, environment, and activities in long-term interactions. The workshop aims to promote a common ground among the relevant scientific communities through invited talks and in-depth discussions via paper presentations, break-out groups, and a scientific debate. In line with the HRI 2023 conference theme, "HRI for all'', our workshop theme is "adaptivity for all'' to encourage HRI theories, methods, designs, and studies for lifelong learning, personalization, and adaptation that aims to promote inclusion and diversity in HRI.
- Olivier A. Blanson Henkemans, Bert P.B. Bierman, Joris Janssen, Mark A. Neerincx, Rosemarijn Looije, Hanneke van der Bosch, and Jeanine A.M. van der Giessen. 2013. Using a robot to personalise health education for children with diabetes type 1: A pilot study. Patient Education and Counseling, Vol. 92, 2 (2013).Google Scholar
- Jiaee Cheong, Sinan Kalkan, and Hatice Gunes. 2023. Causal Structure Learning of Bias for Fair Affect Recognition. In Proc. IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV) Workshops. 340--349.Google ScholarCross Ref
- Nikhil Churamani, Jiaee Cheong, Sinan Kalkan, and Hatice Gunes. 2023. Towards Causal Replay for Knowledge Rehearsal in Continual Learning. In Proc. AAAI Bridge Program on Continual Causality.Google Scholar
- Caitlyn Clabaugh, Kartik Mahajan, Shomik Jain, Roxanna Pakkar, David Becerra, Zhonghao Shi, Eric Deng, Rhianna Lee, Gisele Ragusa, and Maja Matarić. 2019. Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders. Frontiers in Robotics and AI, Vol. 6 (2019).Google ScholarCross Ref
- Caitlyn Clabaugh, Gisele Ragusa, Fei Sha, and Maja Mataric. 2015. Designing a socially assistive robot for personalized number concepts learning in preschool children. In 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). 314--319.Google ScholarCross Ref
- Alessandro Di Nuovo, Frank Broz, Ning Wang, Tony Belpaeme, Angelo Cangelosi, Ray Jones, Raffaele Esposito, Filippo Cavallo, and Paolo Dario. 2018. The multi-modal interface of Robot-Era multi-robot services tailored for the elderly. Intelligent Service Robotics, Vol. 11 (01 2018), 1--18.Google ScholarDigital Library
- Raia Hadsell, Dushyant Rao, Andrei A. Rusu, and Razvan Pascanu. 2020. Embracing Change: Continual Learning in Deep Neural Networks. Trends in Cognitive Sciences, Vol. 24, 12 (Dec. 2020), 1028--1040.Google ScholarCross Ref
- Charlie Hewitt and Hatice Gunes. 2018. CNN-based Facial Affect Analysis on Mobile Devices. arXiv preprint arXiv:1807.08775 (2018).Google Scholar
- Bahar Irfan, Natalia Céspedes, Jonathan Casas, Emmanuel Senft, Carlos A. Cifuentes, Luisa F. Gutiérrez, Mónica Rincon-Roncancio, Tony Belpaeme, and Marcela Múnera. 2022a. Personalised Socially Assistive Robot for Cardiac Rehabilitation: Critical Reflections on Long-Term Interactions in the Real World. User Modeling and User-Adapted Interaction (2022).Google Scholar
- Bahar Irfan, Mehdi Hellou, Alexandre Mazel, and Tony Belpaeme. 2020. Challenges of a Real-World HRI Study with Non-Native English Speakers: Can Personalisation Save the Day?. In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction. ACM, 272--274.Google ScholarDigital Library
- Bahar Irfan, Aditi Ramachandran, Samuel Spaulding, Sinan Kalkan, German Ignacio Parisi, and Hatice Gunes. 2021. Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI). In Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI. 724--727.Google ScholarDigital Library
- Bahar Irfan, Aditi Ramachandran, Samuel Spaulding, German Ignacio Parisi, and Hatice Gunes. 2022b. Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI). In ACM/IEEE International Conference on Human-Robot Interaction, HRI. 1261--1264.Google ScholarCross Ref
- Min Kyung Lee, Jodi Forlizzi, Sara Kiesler, Paul Rybski, John Antanitis, and Sarun Savetsila. 2012. Personalization in HRI: A Longitudinal Field Experiment. In Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction (Boston, Massachusetts, USA) (HRI '12). Association for Computing Machinery, New York, NY, USA, 319--326.Google ScholarDigital Library
- Iolanda Leite, Ginevra Castellano, André Pereira, Carlos Martinho, and Ana Paiva. 2012. Modelling empathic behaviour in a robotic game companion for children: An ethnographic study in real-world settings. HRI'12 - Proceedings of the 7th Annual ACM/IEEE International Conference on Human-Robot Interaction (03 2012).Google ScholarDigital Library
- Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, and Natalia Díaz-Rodríguez. 2020. Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges. Information Fusion, Vol. 58 (2020), 52--68.Google ScholarDigital Library
- German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter. 2019. Continual lifelong learning with neural networks: A review. Neural Networks, Vol. 113 (May 2019), 54--71.Google Scholar
- Judea Pearl. 2009. Causality. Cambridge university press.Google Scholar
- Judea Pearl. 2019. The seven tools of causal inference, with reflections on machine learning. Commun. ACM, Vol. 62, 3 (2019), 54--60.Google ScholarDigital Library
- Davy Preuveneers, Ilias Tsingenopoulos, and Wouter Joosen. 2020. Resource usage and performance trade-offs for machine learning models in smart environments. Sensors, Vol. 20, 4 (2020), 1176.Google ScholarCross Ref
- Laurel D. Riek. 2012. Wizard of Oz Studies in HRI: A Systematic Review and New Reporting Guidelines. J. Hum.-Robot Interact., Vol. 1, 1 (jul 2012), 119--136.Google ScholarDigital Library
- Raquel Ros, Marco Nalin, Rachel Wood, Paul Baxter, Rosemarijn Looije, Yiannis Demiris, Tony Belpaeme, Alessio Giusti, and Clara Pozzi. 2011. Child-robot interaction in the wild: Advice to the aspiring experimenter. ICMI'11 - Proceedings of the 2011 ACM International Conference on Multimodal Interaction, 335--342.Google ScholarDigital Library
- Silvia Rossi, Daniela Conti, Federica Garramone, Gabriella Santangelo, Mariacarla Staffa, Simone Varrasi, and Alessandro Di Nuovo. 2020. The role of personality factors and empathy in the acceptance and performance of a social robot for psychometric evaluations. Robotics, Vol. 9, 2 (2020).Google Scholar
- Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. 2021. Toward causal representation learning. Proc. IEEE, Vol. 109, 5 (2021), 612--634.Google ScholarCross Ref
- Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni. 2020. Green AI. Commun. ACM, Vol. 63, 12 (2020), 54--63.Google ScholarDigital Library
- Samuil Stoychev, Nikhil Churamani, and Hatice Gunes. 2023. Latent Generative Replay for Resource-Efficient Continual Learning of Facial Expressions. In Proc. IEEE Int'l. Conf. on Automatic Face and Gesture Recognition.Google ScholarDigital Library
- Samuil Stoychev and Hatice Gunes. 2022. The Effect of Model Compression on Fairness in Facial Expression Recognition. (2022). showeprint[arXiv]2201.01709Google Scholar
- Lev Vygotsky. 1980 (1930). Interaction between Learning and Development. Vol. 2003. Harvard University Press, Cambridge, MA, Chapter 6.Google Scholar
Index Terms
- Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Adaptivity for All
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