Franki Nguimatsia-Tiofack — accepted AI-conference papers

2026

SVL: Goal-Conditioned Reinforcement Learning as Survival Learning

Franki Nguimatsia-Tiofack, Fabian Schramm, Théotime Le Hellard, Justin Carpentier

ICML 2026poster

Standard approaches to goal-conditioned reinforcement learning (GCRL) that rely on temporal-difference learning can be unstable and sample-inefficient due to bootstrapping. While recent work has explored contrastive and supervised formulations to improve stability, we present a probabilistic alterna…

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2026

Variance-Reduced Model Predictive Path Integral via Quadratic Model Approximation

Fabian Schramm, Franki Nguimatsia-Tiofack, Nicolas Perrin-Gilbert, Marc Toussaint, Justin Carpentier

RSS 2026poster

Sampling-based controllers, such as Model Predictive Path Integral (MPPI) methods, offer substantial flexibility but often suffer from high variance and low sample efficiency. To address these challenges, we introduce a hybrid variance-reduced MPPI framework that integrates a prior model into the sa…

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