5 citations · 5 across the 4 of their papers we have counts for
9 papers
LpWM: A Case for Sparse Representations in World Models
Yilun Kuang, Yash Dagade, Quentin Le Lidec +3
Joint-embedding predictive architectures (JEPAs) learn latent dynamics for planning and avoid representation collapse by matching features to maximum-entropy distributions such as…
LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
Lucas Maes, Quentin Le Lidec, Damien Scieur +2
Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on co…
Accelerating trajectory optimization with Sobolev-trained diffusion policies
Théotime Le Hellard, Franki Nguimatsia Tiofack, Quentin Le Lidec +1
Trajectory Optimization (TO) solvers exploit known system dynamics to compute locally optimal trajectories through iterative improvements. A downside is that each new problem insta…
Causal-JEPA: Learning World Models through Object-Level Latent Masking
Heejeong Nam, Quentin Le Lidec, Lucas Maes +2
World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a useful abstraction, they are not…
stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation
Lucas Maes, Quentin Le Lidec, Luiz Facury +9
World models are central to building agents that can reason, plan, and generalize beyond their training data. However, research on world models is currently fragmented, with dispar…
stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
Lucas Maes, Quentin Le Lidec, Dan Haramati +4
World Models have emerged as a powerful paradigm for learning compact, predictive representations of environment dynamics, enabling agents to reason, plan, and generalize beyond di…