1 citations · 1 across the 9 of their papers we have counts for
10 papers
Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes
Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc +2
On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a frozen or EMA version of themselves…
PRISM: Perception Reasoning Interleaved for Sequential Decision Making
Mohamed Salim Aissi, Clemence Grislain, Clement Romac +4
Scaling LLM-based embodied agents from text-only environments to complex multimodal settings remains a major challenge. Recent work identifies a perception-reasoning-decision gap i…
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,…
Latent Goal Prediction from Language for Model-Based Planning
Samuel Barbeau, Simon Roy, Giovanni Beltrame +2
Joint-Embedding Predictive Architectures (JEPAs) enable agents to plan in latent space by imagining the outcomes of candidate actions, yet task specification remains a bottleneck.…
V-GIFT: Boosting Visual Instruction Tuning with Self-Supervised Guidance
Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc +2
Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-grained visual perception. Rec…
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,…