Nicolas Perrin-Gilbert — accepted AI-conference papers

2026

Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning

Franki NGUIMATSIA TIOFACK, Théotime Le Hellard, Fabian Schramm, Nicolas Perrin-Gilbert, Justin Carpentier

ICLR 2026poster

Offline reinforcement learning often relies on behavior regularization that enforces policies to remain close to the dataset distribution. However, such approaches fail to distinguish between high-value and low-value actions in their regularization components. We introduce Guided Flow Policy (GFP),…

Cited by 5 Source ↗code ↗Scholar ↗

2026

Improving Zero-Shot Offline RL via Behavioral Task Sampling

Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud

ICML 2026poster

Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this problem trains task-conditioned policies by sampling task vectors that define linear reward functions over learned state …

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2026

Reference-Free Sampling-Based Model Predictive Control

Fabian Schramm, Pierre Fabre, Nicolas Perrin-Gilbert, Justin Carpentier

ICRA 2026poster

We present a sampling-based model predictive control (MPC) framework that enables emergent locomotion without relying on handcrafted gait patterns or predefined contact sequences. Our method discovers diverse motion patterns, ranging from trotting to galloping, robust standing policies, jumping, and…

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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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2022

Divide & Conquer Imitation Learning

Alexandre Chenu, Nicolas Perrin-Gilbert, Olivier Sigaud

IROS 2022poster

When cast into the Deep Reinforcement Learning framework, many robotics tasks require solving a long horizon and sparse reward problem, where learning algorithms struggle. In such context, Imitation Learning (IL) can be a powerful approach to bootstrap the learning process. However, most IL methods…

Cited by 7 Source ↗code ↗Scholar ↗