Giuseppe Paolo — accepted AI-conference papers

2025

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl

ICML 2025poster

Pre-trained foundation models (FMs) have shown exceptional performance in univariate time series forecasting tasks. However, several practical challenges persist, including managing intricate dependencies among features and quantifying uncertainty in predictions. This study aims to tackle these crit…

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2025

Zero-shot Model-based Reinforcement Learning using Large Language Models

Abdelhakim Benechehab, Youssef Attia El Hili, Ambroise Odonnat, Oussama Zekri, Albert Thomas, Giuseppe Paolo +3

ICLR 2025poster

The emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks. In reinforcement learning, while LLMs have been extensively used in text-based environments, their integration with continuous state s…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2024

Position: A Call for Embodied AI

Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl

ICML 2024poster

We propose Embodied AI (E-AI) as the next fundamental step in the pursuit of Artificial General Intelligence (AGI), juxtaposing it against current AI advancements, particularly Large Language Models (LLMs). We traverse the evolution of the embodiment concept across diverse fields (philosophy, psycho…

Cited by 6 Source ↗PDF ↗Scholar ↗

2024

SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention

Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov, Aladin Virmaux, Giuseppe Paolo, Themis Palpanas +1

ICML 2024oral

Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting. To better understand this phenomenon, we start by studying a toy linear forecasting proble…

Cited by 17 Source ↗PDF ↗code ↗Scholar ↗

2020

Unsupervised Learning and Exploration of Reachable Outcome Space

Giuseppe Paolo, Alban Laflaquière, Alexandre Coninx, Stephane Doncieux

ICRA 2020poster

Performing Reinforcement Learning in sparse rewards settings, with very little prior knowledge, is a challenging problem since there is no signal to properly guide the learning process. In such situations, a good search strategy is fundamental. At the same time, not having to adapt the algorithm to…

Cited by 35 Source ↗code ↗Scholar ↗

2018

A Data-driven Model for Interaction-Aware Pedestrian Motion Prediction in Object Cluttered Environments

Mark Pfeiffer, Giuseppe Paolo, Hannes Sommer, Juan Nieto, Rol Siegwart, Cesar Cadena

ICRA 2018poster

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior…

Cited by 140 Source ↗Scholar ↗

2017

Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation

Lei Tai, Giuseppe Paolo, Ming Liu

IROS 2017poster

We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser r…

Cited by 951 Source ↗Scholar ↗