[PhD 2024] Modeling joint action in HRI from a bio-inspired approach

Spécialité Automatique, Traitement du signal et des images, Génie informatique
Equipe NEURORHYTHMS
Encadrement de la thèse HENDRY FERREIRA CHAME    Co-Directeur Laure BUHRY 
Starting date  1 october 2024
Application deadline (à 23h59) 30 June 2024
Profile and skills required
Equivalent degree to a French Master II diploma in robotics, signal processing, computer science, mathematical modeling or cognitive science.

Deep Interest in human-robot interaction, embodiment, cognitive sciences and bio-inspired modeling (dynamical systems theory).

Programming skills in Python language (skills in C++ would be a plus).

Notions of classical geometric modeling and behavior regulation in robotics.

Level of French/English required: at least intermediate level. You can speak the language understandably, coherently and confidently on everyday topics that are familiar to you.

APPLICATION
Please prepare the following documents : motivation letter, CV, one recommendation letter and the most recent transcript of your academic records.

Project description
This research project aims at studying the dynamics of joint action in human-robot interaction (HRI) through mathematical modeling, simulations, multi-scale signal processing and prototyping human-robot interaction experiments. Therefore, the main objective of the project is the proposal of a mathematical model allowing to represent and track in real time joint action. The model can contribute to the development of diagnostic methods intended to estimate the quality of interaction in HRI. Given the foreseen intuitive nature of interaction, it can also contribute to the development of methods to study social cognition in several psychological conditions (e.g. autism spectrum disorder, cognitive rehabilitation condition, or psychopathological conditions such as schizophrenia), and to the development of several applications in HRI (educational, assistance, recreational, among others).
References
Amari, S. I. (1977). Dynamics of pattern formation in lateral-inhibition type neural fields. Biological cybernetics, 27(2), 77-87.

Allen, M., & Friston, K. J. (2018). From cognitivism to autopoiesis: towards a computational framework for the embodied mind. Synthese, 195(6), 2459-2482.

Belhassein, K., Fernández-Castro, V., Mayima, A., Clodic, A., Pacherie, E., Guidetti, M., Alami, R., and Cochet, H. (2022). Addressing joint action challenges in HRI: Insights from psychology and philosophy. Acta Psychologica, 222, 103476.

Chame, H. F., Mota, F. P., & da Costa Botelho, S. S. (2019). A dynamic computational model of motivation based on self-determination theory and CANN. Information Sciences, 476, 319-336.

Chame, H. F., & Tani, J. (2020, May). Cognitive and motor compliance in intentional human-robot interaction. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 11291-11297). IEEE.

Chame, H. F., Ahmadi, A., & Tani, J. (2020). A hybrid human-neurorobotics approach to primary intersubjectivity via active inference. Frontiers in Psychology, 11, 584869.

Chame, H. F., Clodic, A., & Alami, R. (2023, May). TOP-JAM: A bio-inspired topology-based model of joint attention for human-robot interaction. In 2023 IEEE International Conference on Robotics and Automation (ICRA).

Fiebich, A., & Gallagher, S. (2013). Joint attention in joint action. Philosophical Psychology, 26(4), 571-587.

Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2013). The anatomy of choice: active inference and agency. Frontiers in human neuroscience, 7, 598.

Gallagher, S. (2008). “Understanding others: embodied social cognition,” in Handbook of Cognitive Science, eds P. Calvo and A. Gomila (San Diego, CA; Oxford; Amsterdam: Elsevier), 437–452.

Newen, A., Bruin, L., and Gallagher, S. (2018). “4E cognition: historical roots, key concepts and central issues,” in The Oxford Handbook of 4E Cognition, eds A. Newen, S. Gallagher, and L. de Bruin (Oxford: Oxford University Press), 3–15.

Vesper, C., Butterfill, S., Knoblich, G., & Sebanz, N. (2010). A minimal architecture for joint action. Neural Networks, 23(8-9), 998-1003.