6 citations · 13 across the 11 of their papers we have counts for
9 papers · 1 filter
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…
Improving Zero-Shot Offline RL via Behavioral Task Sampling
Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud
Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this p…
MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces
Loris Gaven, Thomas Carta, Clément Romac +4
Open-ended learning agents must efficiently prioritize goals in vast possibility spaces, focusing on those that maximize learning progress (LP). When such autotelic exploration is…
From Goal-Conditioned to Language-Conditioned Agents via Vision-Language Models
Theo Cachet, Christopher R. Dance, Olivier Sigaud
Vision-language models (VLMs) have tremendous potential for grounding language, and thus enabling language-conditioned agents (LCAs) to perform diverse tasks specified with text. T…
Help Me Explore: Minimal Social Interventions for Graph-Based Autotelic Agents
Ahmed Akakzia, Olivier Serris, Olivier Sigaud +1
In the quest for autonomous agents learning open-ended repertoires of skills, most works take a Piagetian perspective: learning trajectories are the results of interactions between…
Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms
Alexandre Chenu, Nicolas Perrin-Gilbert, Stéphane Doncieux +1
Reinforcement learning agents need a reward signal to learn successful policies. When this signal is sparse or the corresponding gradient is deceptive, such agents need a dedicated…