Séminaire au DIC: «Lifelong social learning in swarm robotics» par Nicolas Bredeche
8e séminaire au Doctorat en informatique cognitive
Nicolas BRÉDÈCHE - 12 novembre 2020 (ce séminaire sera en anglais)
(LIEN ZOOM - https://uqam.zoom.us/j/96780028011 )
Titre : Lifelong social learning in swarm robotics
In swarm robotics, hundreds or more robots with limited computation and communication capabilities act together to accomplish a common task. Behavior programming is generally done by hand or optimized before deployment to address a specific task (e.g. shape formation) or to produce basic primitives (e.g. leader-following, flocking) that can then be combined by the user. However, it is not always possible to design beforehand the policies required to solve a task, as the environment may be unknown before deployment, or change through time.
In this talk, I will give an overview of our recent works in adaptive swarm robotics, where learning is performed in an online and distributed fashion. I will describe some recent evolutionary and social learning algorithms we have developed and implemented in both simulated and real robots. Originally loosely inspired by evolutionary biology and social learning, these algorithms face challenges that are usually not addressed in robotics, but familiar to biological collective systems. I will describe how the environment shapes learning in unexpected ways, as the swarm must address tasks in a self-sustainable fashion. I will also discuss how to achieve truly cooperative behaviours at the level of the collective, in particular concerning pursuing both individual benefits and social welfare.
Nicolas Bredeche is Professeur des Universités (full professor) in computer science at Sorbonne Université (Campus Pierre et Marie Curie) in Paris, France. His current research activity revolves around adaptive collective systems with two motivations: (1) to understand natural systems, using individual-based modeling and simulation methods (e.g.: collective decision making, evolution of cooperation) and (2) to design adaptive collective/swarm robotic systems using evolutionary and social learning algorithms (e.g.: behavior optimization for collective robotics, online distributed learning for swarm robotics). He is particularly interested in how a collective of individuals, whether artificial or natural, can learn how to survive together in open environments.
Bredeche, J.-M. Montanier, W. Liu, A. FT. Winfield. Environment-driven Distributed Evolutionary Adaptation in a Population of Autonomous Robotic Agents. Mathematical and Computer Modelling of Dynamical Systems, Special Issue: Modelling the swarm – analysing biological and engineered swarm systems. Taylor & Francis Eds. Volume 18, Issue 1, p.101-129, 2012.
Bernard, N. Bredeche, J-B. Andre. Indirect genetic effects allow to escape the inefficient equilibrium in a coordination game. Evolution Letters vol. 4, issue 3, Pages 257-265, June 2020