arXiv subjects

Nicolas Lobato-Dauzier

Publications and source records attributed to Nicolas Lobato-Dauzier.

5 recordsLinked to original sources

arXiv2026

Density and shape govern the dynamical self-organization of active matter on a closed surface

Morphogenesis emerges from dynamic feedback among geometry, mechanics, and chemistry; however, disentangling these contributions in living systems remains challenging. Here, we focus on the interplay between geometry and mechanics by developing a minimal in vitro model in which purified microtubules and kinesin motor clusters self-organize into a two-dimensional active nematic cortex at the surface of spherical water-in-oil droplets. The spherical geometry enforces a total topological charge of +2, here realized by four +1/2 defects whose trajectories reveal robust, self-sustained oscillations. Using full-surface reconstructions, we show that the collective dynamics of the defects lead to a periodic switching between planar and tetrahedral arrangements through alternating coiling and hemisphere-crossing phases. By tuning microtubule density, the system spans a continuum from a classic defect-dominated active nematic to a regime resembling an extensile filament confined to a curved surface, where low density is associated with increased trajectory variability and direction reversals. Geometric perturbations introduced through controlled squeezing redistribute curvature and induce the nucleation of additional defects, thereby reorganizing the entire topological landscape while preserving total charge. Together, these results show that periodic morphogenetic-like cycles, defect topology, and material organization can arise solely from the interplay of activity, density, and curvature. This reconstituted system provides a versatile platform for elucidating the coupling between mechanics and geometry underlying shape formation in active biological matter.

cond-mat.soft↗

arXiv2024

Confinement determines transport of a reaction-diffusion active matter front

Couplings between biochemical and mechanical processes have a profound impact on embryonic development. However, in-vitro studies capable of quantifying these interactions have remained elusive. Here, we investigate a synthetic system where a DNA reaction-diffusion (RD) front is advected by a turbulent flow generated by active matter (AM) flows in a quasi-one-dimensional geometry. Whereas the dynamics of simple RD fronts solely depend on the reaction and diffusion rates, we show that RD-AM front propagation is also influenced by the confinement geometry. We first experimentally dissected the different components of the reaction-diffusion-advection process by knocking out reaction or advection and observed how RD-AM allows for faster transport over large distances, avoiding dilution. We then show how confinement impacts active matter flow: while changes in instantaneous flow velocities are small; correlation times are dramatically increased with decreasing confinement. As a result, RD-AM front speed increased up to 8-fold compared to a RD one, in quantitative agreement with a conveyor belt RDA theoretical model. The RD-AM experimental system described here provides a framework for the rational engineering of complex spatiotemporal processes observed in living systems. It will reinforce our understanding of how macro-scale patterns and structures emerge from microscopic components in non-equilibrium systems.

cond-mat.soft↗

arXiv2024

Signaling and Social Learning in Swarms of Robots

This paper investigates the role of communication in improving coordination within robot swarms, focusing on a paradigm where learning and execution occur simultaneously in a decentralized manner. We highlight the role communication can play in addressing the credit assignment problem (individual contribution to the overall performance), and how it can be influenced by it. We propose a taxonomy of existing and future works on communication, focusing on information selection and physical abstraction as principal axes for classification: from low-level lossless compression with raw signal extraction and processing to high-level lossy compression with structured communication models. The paper reviews current research from evolutionary robotics, multi-agent (deep) reinforcement learning, language models, and biophysics models to outline the challenges and opportunities of communication in a collective of robots that continuously learn from one another through local message exchanges, illustrating a form of social learning.

cs.RO↗

arXiv2024

Hearing the shape of an arena with spectral swarm robotics

Swarm robotics promises adaptability to unknown situations and robustness against failures. However, it still struggles with global tasks that require understanding the broader context in which the robots operate, such as identifying the shape of the arena in which the robots are embedded. Biological swarms, such as shoals of fish, flocks of birds, and colonies of insects, routinely solve global geometrical problems through the diffusion of local cues. This paradigm can be explicitly described by mathematical models that could be directly computed and exploited by a robotic swarm. Diffusion over a domain is mathematically encapsulated by the Laplacian, a linear operator that measures the local curvature of a function. Crucially the geometry of a domain can generally be reconstructed from the eigenspectrum of its Laplacian. Here we introduce spectral swarm robotics where robots diffuse information to their neighbors to emulate the Laplacian operator - enabling them to "hear" the spectrum of their arena. We reveal a universal scaling that links the optimal number of robots (a global parameter) with their optimal radius of interaction (a local parameter). We validate experimentally spectral swarm robotics under challenging conditions with the one-shot classification of arena shapes using a sparse swarm of Kilobots. Spectral methods can assist with challenging tasks where robots need to build an emergent consensus on their environment, such as adaptation to unknown terrains, division of labor, or quorum sensing. Spectral methods may extend beyond robotics to analyze and coordinate swarms of agents of various natures, such as traffic or crowds, and to better understand the long-range dynamics of natural systems emerging from short-range interactions.

cs.RO↗

arXiv2015

Geometric pumping in autophoretic channels

Many microfluidic devices use macroscopic pressure differentials to overcome viscous friction and generate flows in microchannels. In this work, we investigate how the chemical and geometric properties of the channel walls can drive a net flow by exploiting the autophoretic slip flows induced along active walls by local concentration gradients of a solute species. We show that chemical patterning of the wall is not required to generate and control a net flux within the channel, rather channel geometry alone is sufficient. Using numerical simulations, we determine how geometric characteristics of the wall influence channel flow rate, and confirm our results analytically in the asymptotic limit of lubrication theory.

physics.flu-dyn↗