Back-end Developer in Paris, France

Swarm robotics studies how a large number of relatively simple robots can accomplish various functions collectively and dynamically. Modular robotics concentrates on the design of specialized connected parts to perform precise tasks, while other swarms exhibit more fluid flocking and group adaptation.

In this project, we focused on the process of morphogenesis per se, i.e., the programmable and reliable bottom-up emergence of shapes at a higher level of organization. We showed that simple abstract rules of behavior executed by each agent or their genotype, involving message passing, virtual link creation, and force-based motion, are sufficient to generate various reproducible and scalable multi-agent branched structures or phenotypes. On this basis, we proposed a model of collective robot dynamics based on morphogenetic engineering principles, in particular an algorithm of programmable network growth, and how it allowed a flock of self-propelled wheeled robots on the ground to coordinate and function together.

The model was implemented in simulation and demonstrated in physical experiments with Psi Swarm robots.

Focusing on the challenge of fostering the self-assembly of socio-technical networks, we presented the application of Morphogenetic Engineering principles in this domain to a 2D spatial case study involving a team of first responders. Our model and simulation illustrated how rescue team members could be guided via hand-held devices toward better coordination and positioning at appropriate locations, based on peer-to-peer communication and local landmarks in the environment, such as incidents or exits, without the need for a centralized control center. Using Raspberry Pi devices, we illustrated this scenario in various situations that required quick decision-making to control and manage. Our work suggested the possibility of novel forms of bottom-up self-organization among groups of users and machines, in contrast to top-down imposed hierarchies and policies.

The study of multicellular development is grounded in two complementary domains: cell biomechanics, which examines how physical forces shape the embryo, and genetic regulation and molecular signaling, which concern how cells determine their states and behaviors. Integrating both sides into a unified framework is crucial to fully understand the self-organized dynamics of morphogenesis. In this project, we introduced MecaGen, an integrative modeling platform enabling the hypothesis-driven simulation of these dual processes via the coupling between mechanical and chemical variables. Our approach relied upon a minimal cell behavior ontology comprising mesenchymal and epithelial cells and their associated behaviors. MecaGen enabled the specification and control of complex collective movements in 3D space through a biologically relevant gene regulatory network and parameter space exploration. Three case studies investigating pattern formation, epithelial differentiation, and tissue tectonics in zebrafish early embryogenesis, the latter with quantitative comparison to live imaging data, demonstrated the validity and usefulness of our framework.