Autonomous robots still face major challenges when navigating in complex natural environments under strong constraints of energy, computation, sensing, and communication. In contrast, insects such as ants and bees achieve remarkable navigation abilities using miniature brains and highly frugal visual systems. Understanding these mechanisms offers a promising route toward the development of lightweight and robust autonomous machines capable of operating in GNSS-denied and communication-limited environments. In this talk, I will present a series of bio-inspired robotic systems and computational models investigating how insect visual navigation principles can be translated into autonomous robots. I will focus on route following, visual homing, and teach-and-repeat strategies based on insect behavior and brain-inspired neural architectures using ultra-low-resolution vision. These approaches have been implemented and validated on multiple robotic platforms, including car-like robots, micro aerial vehicles, and more recently humanoid robots, in both controlled and natural environments. In particular, I will show how robust navigation can emerge from minimal sensory and computational resources, with systems operating onboard at 15 Hz on a Raspberry Pi in Python, using only 32 × 32 pixel visual inputs, memory footprints as low as 0.3 MB per kilometer, and achieving a median lateral error of 25 cm over more than 1.6 km of real-world routes. Beyond their engineering applications, these systems provide a computational framework to test hypotheses about animal cognition, visual memory, and decision-making, while more broadly exploring how frugal intelligence can emerge from the interaction between perception, memory, and action, contributing to both autonomous robotics and the understanding of biological intelligence.

Bio : Gabriel Gattaux is a PhD candidate in Robotics at Aix-Marseille University with a focus on bio-inspired robotics, working at the intersection of autonomous robotics, neuromorphic engineering, natural and artificial vision, control theory, computational neuroscience, and ethology. His research aims to understand how biological systems, particularly insects such as ants, bees, and flies, perceive, navigate, and make decisions in natural environments, and to translate these principles into frugal and robust autonomous machines. His work has recently been published in Nature Communications and IEEE Robotics and Automation Letters. His doctoral work is conducted under the supervision of Franck Ruffier, Julien Serres, and Antoine Wystrach, in collaboration with the Centre de Recherche sur la Cognition Animale (CNRS, Toulouse), the Joint Robotics Laboratory (CNRS-AIST, Japan), and ENSTA Bretagne. He holds an Engineering Degree in Mechatronics from the ENSIL-ENSCI Engineering School at the University of Limoges, including an exchange in Computer Science at Wrocław University of Science and Technology. Prior to this, he earned a University Degree in Technology (DUT) in Mechanical and Production Engineering from the Nancy-Brabois Institute of Technology, alongside a dual curriculum with Polytech Nancy.


Website : https://gaby-253.github.io