6 citations · 20 across the 26 of their papers we have counts for
9 papers · 1 filter
Time to Play: Simulating Early-Life Animal Dynamics Enhances Robotics Locomotion Discovery
Paul Templier, Hannah Janmohamed, David Labonte +1
Developmental changes in body morphology profoundly shape locomotion in animals, yet artificial agents and robots are typically trained under static physical parameters. Inspired b…
From Tabula Rasa to Emergent Abilities: Discovering Robot Skills via Real-World Unsupervised Quality-Diversity
Luca Grillotti, Lisa Coiffard, Oscar Pang +2
Autonomous skill discovery aims to enable robots to acquire diverse behaviors without explicit supervision. Learning such behaviors directly on physical hardware remains challengin…
A Path to Universal Neural Cellular Automata
Gabriel Béna, Maxence Faldor, Dan F. M. Goodman +1
Cellular automata have long been celebrated for their ability to generate complex behaviors from simple, local rules, with well-known discrete models like Conway's Game of Life pro…
Overcoming Deceptiveness in Fitness Optimization with Unsupervised Quality-Diversity
Lisa Coiffard, Paul Templier, Antoine Cully
Policy optimization seeks the best solution to a control problem according to an objective or fitness function, serving as a fundamental field of engineering and research with appl…
Multi-Objective Quality-Diversity in Unstructured and Unbounded Spaces
Hannah Janmohamed, Antoine Cully
Quality-Diversity algorithms are powerful tools for discovering diverse, high-performing solutions. Recently, Multi-Objective Quality-Diversity (MOQD) extends QD to problems with s…
Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy, Stochastic or Uncertain Domains
Manon Flageat, Johann Huber, François Helenon +2
Quality-Diversity (QD) has demonstrated potential in discovering collections of diverse solutions to optimisation problems. Originally designed for deterministic environments, QD h…
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization
Maxence Faldor, Robert Tjarko Lange, Antoine Cully
Quality-Diversity has emerged as a powerful family of evolutionary algorithms that generate diverse populations of high-performing solutions by implementing local competition princ…
Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity
Ryan Bahlous-Boldi, Maxence Faldor, Luca Grillotti +4
Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution. While…
Scaling Policy Gradient Quality-Diversity with Massive Parallelization via Behavioral Variations
Konstantinos Mitsides, Maxence Faldor, Antoine Cully
Quality-Diversity optimization comprises a family of evolutionary algorithms aimed at generating a collection of diverse and high-performing solutions. MAP-Elites (ME), a notable e…