The innovations that gave rise to industrial robots date back to the sixties and seventies. They have enabled a massive deployment of industrial robots that transformed factory floors, at least in industrial sectors such as car manufacturing and other mass production lines.

However, such robots do not fit the requirements of other interesting applications that appeared and developed in fields such as in laboratory research, space robotics, medical robotics, automation in inspection and maintenance, agricultural robotics, service robotics and, of course, humanoids. A small number of these sectors have seen large-scale deployment and commercialization of robotic systems, with most others advancing slowly and incrementally to that goal.

This begs the following question: is it due to unsuitable hardware (insufficient physical capabilities to generate the required motions and forces); software capabilities (control systems, perception, decision support, learning, etc.); or a lack of new design paradigms capable to meet the needs of these applications (agile and scalable custom-design approaches)?

The unprecedented explosion of data science, machine learning and AI in all areas of science, technology and society may be seen as a compelling solution, and a radical transformation is taking shape (or is anticipated), with the promise of empowering the next generations of robots with AI (both predictive and generative). Therefore, research can tend to pay increasing attention to the software aspects (learning, decision support, coding etc.); perhaps to the detriment of more advanced physical capabilities (hardware) and new concepts (design paradigms). It is however clear that the cognitive aspects of robotics, including learning, control and decision support, are useful if and only if suitable physical embodiments are available to meet the needs of the various tasks that can be robotized, hence requiring adapted design methodologies and hardware.

The aim of this thesis is thus to focus on design paradigms and hardware, and in particular on the optimal design of rapidly-produced serial robots based on given families of standardized « modules » whose layout will be optimized according to the requirements of the tasks that cannot be performed by the industrial robots available on the market. The ambition is to answer the question of whether and how a paradigm shift may be possible for the design of robots, from being fixed-catalogue to rapidly available bespoke type.

The successful candidate will enrol at the « Ecole Doctorale Mathématiques, STIC » of Nantes Université (ED-MASTIC), and he or she will be hosted for three years in the CEA-LIST Interactive Robotics Unit under supervision of Dr Farzam Ranjbaran. Professors Yannick Aoustin (Nantes) and Clément Gosselin (Laval) will provide academic guidance and joint supervision for a successful completion of the thesis.

A follow-up to this thesis is strongly considered in the form of a one-year Post-Doctoral fellowship to which the candidate will be able to apply, upon successful completion of all the requirements of the PhD Degree. This Post-Doctoral fellowship will be hosted at the « Centre de recherche en robotique, vision et intelligence machine (CeRVIM) », Université Laval, Québec, Canada.