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THUS The Future

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CHAINS | SYNERGY | MEGALOPOLIS | UNBIAS

SYNERGY Team

Computer Science and Automation together to create Techno-Human systems

Leaders

🔗 Maíra Martins Silva

🇧🇷 Brazilian Leader

Associate Professor

São Carlos School of Engineering, University of São Paulo

LinkedIn

🔗 Khátia Marçal de Oliveira

🇫🇷 French Leader

Professor

Université Polytechnique Hauts-de-France

Vision

Develop autonomous system according to user needs and preferences, enabling autonomous adaptation to characteristics, attributes, or features unique to individuals.

Goals

This project aims at contributing to these challenges by proposing new modelling and control strategies for autonomous systems considering users in the design and evaluation process. Our objectives are more specifically to:

  • Devise low-complexity learning and data-driven modeling strategies. Often, a large amount of data is available, and it is mandatory to extract only the information crucial to deriving a reliable model of reduced complexity for analysis or control purposes;

  • Develop safe reinforcement learning control strategies. The features of reinforcement learning make it a relevant candidate for controlling dynamical systems due to its ability to adapt control actions to system variations. However, endowing systems controlled by reinforcement learning with stability and robustness guarantees remains a largely open question;  

  • Define a model-based solutions that, by estimation of unmeasurable variables, can enrich learning abilities; 

  • Define procedures for the development and evaluation of autonomous systems, taking particular needs into account;

  • Develop a decision support  architecture that accurately detects and efficiently responds to safety and security problems for autonomous systems; and

  • Develop appropriate sensor technology, exploit estimation techniques including unknown input observers, derive precise real-time physical and data-driven based dynamic models, and implement control strategies exploring artificial intelligence methods are key elements for achieving adaptive viable implementations. Strategies that can meet individual-specific requirements for assistive to autonomous systems are still an open research field.

Members

  • 🔗 Adenilso Simão, Professor at ICMC USP

  • 🔗 André P.L.F. Carvalho, Professor at ICMC USP

  • 🔗 Antoine Gallais, Professor at the Université Polytechnique Hauts-de-France

  • 🔗 Bruna Carolina Rodrigues da Cunha, Professor at ICMC USP

  • 🔗 Cássio Guimarães Lopes, Professor at at Poli USP

  • 🔗 Catherine Dezan, Professor at the Université de Bretagne Occidentale

  • 🔗 David Espes, Professor at the Université de Bretagne Occidentale

  • 🔗 Hamza Ouarnoughi, Professor at the Université Polytechnique Hauts-de-France

  • 🔗 Jamal Daafouz, Professor at the Université de Lorraine

  • 🔗 João Cavalcanti Santos, Professor at the Université de Montpellier

  • 🔗 José Henrique de M. Goulart, Professor at the Toulouse INP

  • 🔗 Kalinka R.L. J. C. Branco, Professor at ICMC USP 

  • 🔗 Kamila Rios Rodrigues, Professor at ICMC USP

  • 🔗 Márcia da Costa Peixoto, Tenure track position at the Université Polytechnique Hauts-de-France

  • 🔗 Marie Chabert, Professor at the Toulouse INP

  • 🔗 Oswaldo L. V. Costa, Professor at Poli USP

  • 🔗 Phillip M. S. Burt, Professor at Poli USP

  • 🔗 Rafik Belloum, Professor at the Université Polytechnique Hauts-de-France

  • 🔗 Romain Postoyan, Directeur de Recherche at CRAN

  • 🔗 Thierry-Marie Guerra, Professor at the Université Polytechnique Hauts-de-France

  • 🔗 Youcef Imine, Professor at the Université Polytechnique Hauts-de-France


Be part of the team

We welcome new research topics and axis for our project.

Get in touch with us!

2025   |   USP - CNRS   |   Brazil - France

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