13 citations · 58 across the 14 of their papers we have counts for
15 papers
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery
Robert Tjarko Lange, Luca Grillotti, Yutaro Yamada +2
Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, analyze their results, and auton…
Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks
Yutaro Yamada, Luca Grillotti, Rujikorn Charakorn +3
Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory. Artificial networks that respect this constraint must coordinate se…
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…
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…
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…
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…
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
Felix Chalumeau, Bryan Lim, Raphael Boige +7
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization…
Benchmark tasks for Quality-Diversity applied to Uncertain domains
Manon Flageat, Luca Grillotti, Antoine Cully
While standard approaches to optimisation focus on producing a single high-performing solution, Quality-Diversity (QD) algorithms allow large diverse collections of such solutions…
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains
Luca Grillotti, Manon Flageat, Bryan Lim +1
Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the pr…
Discovering Unsupervised Behaviours from Full-State Trajectories
Luca Grillotti, Antoine Cully
Improving open-ended learning capabilities is a promising approach to enable robots to face the unbounded complexity of the real-world. Among existing methods, the ability of Quali…
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning
Manon Flageat, Bryan Lim, Luca Grillotti +3
We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control. The suite includes the definition of tasks, environments…
Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms
Luca Grillotti, Antoine Cully
Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving do…
Accelerated Quality-Diversity through Massive Parallelism
Bryan Lim, Maxime Allard, Luca Grillotti +1
Quality-Diversity (QD) optimization algorithms are a well-known approach to generate large collections of diverse and high-quality solutions. However, derived from evolutionary com…
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires
Bryan Lim, Luca Grillotti, Lorenzo Bernasconi +1
Quality-Diversity (QD) algorithms are powerful exploration algorithms that allow robots to discover large repertoires of diverse and high-performing skills. However, QD algorithms…
Unsupervised Behaviour Discovery with Quality-Diversity Optimisation
Luca Grillotti, Antoine Cully
Quality-Diversity algorithms refer to a class of evolutionary algorithms designed to find a collection of diverse and high-performing solutions to a given problem. In robotics, suc…