16 papers
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…
Onboard MuJoCo-based Model Predictive Control for Shipboard Crane with Double-Pendulum Sway Suppression
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…
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…
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…