Timotéo Carletti

About

Timotéo Carletti is a leading researcher in collective decision-making and swarm robotics, with a focus on how groups of agents—whether animals, plants, or autonomous robots—achieve efficient consensus. His most-cited work, “Speed-accuracy trade-offs in best-of-*n* collective decision making through heterogeneous mean-field modeling” (2024, 9 citations), introduces a mathematical framework to analyze how groups balance the need for quick decisions against the risk of errors when selecting among multiple options of varying quality. This contribution is pivotal for understanding natural systems, from animal groups to fungi, and for designing robust algorithms in swarm robotics. Carletti also explores practical applications, such as in “Odometry During Object Transport: A Study with Swarm of Physical Robots” (2021, 3 citations), where he investigates how robots coordinate movement without centralized control. Additionally, his work on “Learning Multiple Conflicting Tasks with Artificial Evolution” (2014, 3 citations) addresses challenges in evolving adaptive behaviors for complex environments. With a career spanning theoretical modeling and experimental robotics, Carletti’s research advances both fundamental knowledge and real-world autonomous systems, making him a key figure in the field.

Research Focus

Key Achievements

Top Papers

1. 1
   Speed-accuracy trade-offs in best-of-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> collective decision making through heterogeneous mean-field modeling

   9 citations · 2024
2. 2
   Odometry During Object Transport: A Study with Swarm of Physical Robots

   3 citations · 2021
3. 3
   Learning Multiple Conflicting Tasks with Artificial Evolution

   3 citations · 2014

Key Collaborators

Contact & Links