6 citations · 20 across the 26 of their papers we have counts for
27 papers
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework
Hannah Janmohamed, Joao Correia, Penousal Machado +1
Denoising Diffusion Probabilistic Models have emerged as powerful generative priors across domains ranging from molecular design to high-resolution image synthesis. However, standa…
Sampling-Based MuJoCo MPC for Double-Pendulum Sway Suppression on a Shipboard Crane
Oscar Pang, Lisa Coiffard, Paul Templier +3
Transferring heavy payloads in maritime settings relies on efficient crane operation, limited by hazardous double-pendulum payload sway. This sway motion is further exacerbated in…
Dreaming in Code for Curriculum Learning in Open-Ended Worlds
Konstantinos Mitsides, Maxence Faldor, Antoine Cully
Open-ended learning frames intelligence as emerging from continual interaction with an ever-expanding space of environments. While recent advances have utilized foundation models t…
CODE-SHARP: Continuous Open-ended Discovery and Evolution of Skills as Hierarchical Reward Programs
Richard Bornemann, Pierluigi Vito Amadori, Antoine Cully
A core quality of general intelligence is the ability to open-endedly expand and evolve its set of mastered skills autonomously. While recent Foundation Model (FM) driven approache…
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…
Preference-Conditioned Gradient Variations for Multi-Objective Quality-Diversity
Hannah Janmohamed, Maxence Faldor, Thomas Pierrot +1
In a variety of domains, from robotics to finance, Quality-Diversity algorithms have been used to generate collections of both diverse and high-performing solutions. Multi-Objectiv…
CAX: Cellular Automata Accelerated in JAX
Maxence Faldor, Antoine Cully
Cellular automata have become a cornerstone for investigating emergence and self-organization across diverse scientific disciplines. However, the absence of a hardware-accelerated…
Exploring the Performance-Reproducibility Trade-off in Quality-Diversity
Manon Flageat, Hannah Janmohamed, Bryan Lim +1
Quality-Diversity (QD) algorithms have exhibited promising results across many domains and applications. However, uncertainty in fitness and behaviour estimations of solutions rema…
Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity
Maxence Faldor, Antoine Cully
From the formation of snowflakes to the evolution of diverse life forms, emergence is ubiquitous in our universe. In the quest to understand how complexity can arise from simple ru…
Genetic Drift Regularization: on preventing Actor Injection from breaking Evolution Strategies
Paul Templier, Emmanuel Rachelson, Antoine Cully +1
Evolutionary Algorithms (EA) have been successfully used for the optimization of neural networks for policy search, but they still remain sample inefficient and underperforming in…
Quality with Just Enough Diversity in Evolutionary Policy Search
Paul Templier, Luca Grillotti, Emmanuel Rachelson +2
Evolution Strategies (ES) are effective gradient-free optimization methods that can be competitive with gradient-based approaches for policy search. ES only rely on the total episo…
OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code
Maxence Faldor, Jenny Zhang, Antoine Cully +1
Open-ended and AI-generating algorithms aim to continuously generate and solve increasingly complex tasks indefinitely, offering a promising path toward more general intelligence.…
Large Language Models as In-context AI Generators for Quality-Diversity
Bryan Lim, Manon Flageat, Antoine Cully
Quality-Diversity (QD) approaches are a promising direction to develop open-ended processes as they can discover archives of high-quality solutions across diverse niches. While alr…
Illuminating the property space in crystal structure prediction using Quality-Diversity algorithms
Marta Wolinska, Aron Walsh, Antoine Cully
The identification of materials with exceptional properties is an essential objective to enable technological progress. We propose the application of \textit{Quality-Diversity} alg…
Multi-Objective Quality-Diversity for Crystal Structure Prediction
Hannah Janmohamed, Marta Wolinska, Shikha Surana +3
Crystal structures are indispensable across various domains, from batteries to solar cells, and extensive research has been dedicated to predicting their properties based on their…
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
Luca Grillotti, Maxence Faldor, Borja G. León +1
A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforce…