1 citations · 1 across the 9 of their papers we have counts for
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MODIP: Efficient Model-Based Optimization for Diffusion Policies
Zakariae El Asri, Philippe Gratias-Quiquandon, Nicolas Thome +1
Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC). However,…
Revisiting the Learning Objectives of Vision-Language Reward Models
Simon Roy, Samuel Barbeau, Giovanni Beltrame +2
Learning generalizable reward functions is a core challenge in embodied intelligence. Recent work leverages contrastive vision language models (VLMs) to obtain dense, domain-agnost…
RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms
Zakariae El Asri, Ibrahim Laiche, Clément Rambour +2
Learning a controller directly on the robot requires extreme sample efficiency. Model-based reinforcement learning (RL) methods are the most sample efficient, but they often suffer…
VIPER: Visual Perception and Explainable Reasoning for Sequential Decision-Making
Mohamed Salim Aissi, Clemence Grislain, Mohamed Chetouani +3
While Large Language Models (LLMs) excel at reasoning on text and Vision-Language Models (VLMs) are highly effective for visual perception, applying those models for visual instruc…
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting
Mohamed Salim Aissi, Clement Romac, Thomas Carta +5
Reinforcement learning (RL) is a promising approach for aligning large language models (LLMs) knowledge with sequential decision-making tasks. However, few studies have thoroughly…
Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning
Zakariae El Asri, Olivier Sigaud, Nicolas Thome
Applying reinforcement learning (RL) to real-world applications requires addressing a trade-off between asymptotic performance, sample efficiency, and inference time. In this work,…