Pierre Fournier — accepted AI-conference papers

2025

Unsupervised Discovery of Objects Physical Properties Through Maximum Entropy Reinforcement Learning

Maxime Chareyre, Pierre Fournier, Julien Moras, Jean-Marc Bourinet, Youcef Mezouar

RA-L 2025

Understanding the environment is crucial for autonomous robots to perform navigation and manipulation tasks. Never-seen-before objects may have complex appearances and dynamics, where only physical interactions can help to identify visually hidden properties like mass or friction. In this work we pr

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2019

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

Cédric Colas, Pierre Fournier, Mohamed Chetouani, Olivier Sigaud, Pierre-Yves Oudeyer

ICML 2019oral

In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a large diversity of goals, aiming to discover what is controllable in their environments, and what is not. Because some go…

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